Kangning Cui

Research

Efficient, reliable and interpretable machine learning for language models, geospatial observation, and medical images and signals. My work combines model compression, uncertainty estimation, and learning with limited labels.

Scholar profile: Google Scholar|† first author|* corresponding author

Research Themes

Efficient & Reliable ML

Compression, efficient inference, and reliable behavior in language and generative models.

Explore research

Geospatial AI

Learning from aerial and satellite imagery to map forests, monitor change, and support agriculture.

Explore research

Medical AI

Efficient adaptation and learning with limited annotations for biomedical images and signals.

Explore research

Selected Journal Publications

Center-guided Classifier for Semantic Segmentation of Remote Sensing Images[TGRS'26]

Wei Zhang, Mengting Ma, Yizhen Jiang, Yun Chen, Zhenhua Huang, Wangyu Wu, Kangning Cui, Rongrong Lian, Zhenkai Wu, and Xiaowen Ma*

Paper PDF (preprint) Preprint Code
BibTeX
@article{zhang2026centerseg,
  author = {Zhang, Wei and
            Ma, Mengting and
            Jiang, Yizhen and
            Chen, Yun and
            Huang, Zhenhua and
            Wu, Wangyu and
            Cui, Kangning and
            Lian, Rongrong and
            Wu, Zhenkai and
            Ma, Xiaowen},
  title = {Center-Guided Classifier for Semantic Segmentation of Remote Sensing Images},
  journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
  year = {2026},
  volume = {64},
  pages = {1--16},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  doi = {10.1109/tgrs.2026.3657453},
  url = {https://doi.org/10.1109/tgrs.2026.3657453},
}
Details
Figure for CenterSeg

Abstract

Compared with natural images, remote sensing images (RSIs) have the unique characteristic, i.e., larger intraclass variance, which makes semantic segmentation for remote sensing images more challenging. Moreover, existing semantic segmentation models for remote sensing images usually employ a vanilla softmax classifier, which has three drawbacks: (1) non-direct supervision for the pixel representations during training; (2) inadequate modeling ability of parametric softmax classifiers under large intraclass variance; and (3) opaque process of classification decision. In this paper, we propose a novel classifier (called CenterSeg) customized for RSI semantic segmentation, which solves the abovementioned problems with multiple prototypes, direct supervision under Grassmann manifold, and interpretability strategy. Specifically, for each class, our CenterSeg obtains local class centers by aggregating corresponding pixel features based on ground-truth masks, and generates multiple prototypes through hard attention assignment and momentum updating. In addition, we introduce the Grassmann manifold and constrain the joint embedding space of pixel features and prototypes based on two additional regularization terms. Especially, during the inference, CenterSeg can further provide interpretability to the model by restricting the prototype as a sample of the training set. Experimental results on three remote sensing segmentation datasets validate the effectiveness of the model. Besides the superior performance, CenterSeg has the advantages of simplicity, lightweight, compatibility, and interpretability.

Efficient Localization and Spatial Distribution Modeling of Canopy Palms Using UAV Imagery[TGRS'25]

Kangning Cui†,*, Wei Tang†, Rongkun Zhu, Manqi Wang, Gregory D. Larsen, Victor P. Pauca, Sarra Alqahtani, Fan Yang, David Segurado, Paul Fine, Jordan Karubian, Raymond H. Chan, Robert J. Plemmons, Jean-Michel Morel, and Miles R. Silman

Paper PDF (preprint) Preprint Dataset Code
BibTeX
@article{cui2025palmstgrs,
  author = {Cui, Kangning and
            Tang, Wei and
            Zhu, Rongkun and
            Wang, Manqi and
            Larsen, Gregory D. and
            Pauca, Victor P. and
            Alqahtani, Sarra and
            Yang, Fan and
            Segurado, David and
            Fine, Paul and
            Karubian, Jordan and
            Chan, Raymond H. and
            Plemmons, Robert J. and
            Morel, Jean-Michel and
            Silman, Miles R.},
  title = {Efficient Localization and Spatial Distribution Modeling of Canopy Palms Using {UAV} Imagery},
  journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
  year = {2025},
  volume = {63},
  pages = {1--15},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  doi = {10.1109/tgrs.2025.3584093},
  url = {https://doi.org/10.1109/tgrs.2025.3584093},
}
Details
PalmDSNet workflow: UAV imagery, palm detection, segmentation, counting, and saliency maps

Abstract

Understanding the spatial distribution of palms in tropical forests is essential for ecological monitoring, conservation strategies, and the sustainable integration of natural forest products into local and global supply chains. However, the analysis of remotely sensed data are challenged by overlapping palm and tree crowns, uneven shading across the canopy surface, and the heterogeneous nature of the forest landscapes, which often affect the performance of palm detection and segmentation algorithms. To overcome these issues, we introduce PalmDSNet, a deep learning framework for efficient detection, segmentation, and counting of canopy palms. To model spatial patterns, we introduce a bimodal reproduction algorithm that simulates palm propagation based on PalmDSNet outputs. We used UAV-captured imagery to create orthomosaics from 21 sites across western Ecuadorian tropical forests, covering a gradient from the everwet Choco forests near Colombia to the drier forests of southwestern Ecuador. Expert annotations were used to create a comprehensive dataset, including 7,356 bounding boxes on image patches and 7,603 palm centers across five orthomosaics, encompassing a total area of 449 hectares. By integrating detection and spatial modeling, we effectively simulate the spatial distribution of palms in diverse and dense tropical environments, validating its utility for advanced applications in tropical forest monitoring and remote sensing analysis. The dataset can be accessed at 10.5281/zenodo.13822508, and the code to replicate the study is available at github.com/ckn3/palm-ds-sp.

Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering[TGRS'24]

Kangning Cui†,*, Ruoning Li, Sam L. Polk, Yinyi Lin, Hongsheng Zhang, James M. Murphy, Robert J. Plemmons, and Raymond H. Chan

Paper PDF (preprint) Preprint Code
BibTeX
@article{cui2024s2dl,
  author = {Cui, Kangning and
            Li, Ruoning and
            Polk, Sam L and
            Lin, Yinyi and
            Zhang, Hongsheng and
            Murphy, James M. and
            Plemmons, Robert J. and
            Chan, Raymond H.},
  title = {Superpixel-Based and Spatially Regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering},
  journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
  year = {2024},
  volume = {62},
  pages = {1--18},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  doi = {10.1109/tgrs.2024.3385202},
  url = {https://doi.org/10.1109/tgrs.2024.3385202},
}
Details
S2DL workflow: hyperspectral data, superpixels, diffusion geometry, and clustering without labels

Abstract

Hyperspectral images (HSIs) provide exceptional spatial and spectral resolution of a scene, crucial for various remote sensing applications. However, the high dimensionality, presence of noise and outliers, and the need for precise labels of HSIs present significant challenges to the analysis of HSIs, motivating the development of performant HSI clustering algorithms. This paper introduces a novel unsupervised HSI clustering algorithm, Superpixel-based and Spatially-regularized Diffusion Learning (S2DL), which addresses these challenges by incorporating rich spatial information encoded in HSIs into diffusion geometry-based clustering. S2DL employs the Entropy Rate Superpixel (ERS) segmentation technique to partition an image into superpixels, then constructs a spatially-regularized diffusion graph using the most representative high-density pixels. This approach reduces computational burden while preserving accuracy. Cluster modes, serving as exemplars for underlying cluster structure, are identified as the highest-density pixels farthest in diffusion distance from other highest-density pixels. These modes guide the labeling of the remaining representative pixels from ERS superpixels. Finally, majority voting is applied to the labels assigned within each superpixel to propagate labels to the rest of the image. This spatial-spectral approach simultaneously simplifies graph construction, reduces computational cost, and improves clustering performance. S2DL’s performance is illustrated with extensive experiments on four publicly available, real-world HSIs: Indian Pines, Salinas, Salinas A, and WHU-Hi. Additionally, we apply S2DL to landscape-scale, unsupervised mangrove species mapping in the Mai Po Nature Reserve, Hong Kong, using a Gaofen-5 HSI. The success of S2DL in these diverse numerical experiments indicates its efficacy on a wide range of important unsupervised remote sensing analysis tasks.

Selected Conference Publications

LowRankArena: A Standardized Evaluation Platform for SVD-Based LLM Compression[NeurIPS'26, ED Track, Poster]

Zishan Shao†, Lixun Zhang†, Kangning Cui†, Wenhao Wu†, Jinhee Kim, Yixiao Wang, Ting Jiang, Hancheng Ye, Qinsi Wang, Fan Yang, Danyang Zhuo, Hai Li, and Yiran Chen

Paper Preprint PDF (preprint) Code Project Page
BibTeX
@inproceedings{shao2026lowrankarena,
  author = {Shao, Zishan and
            Zhang, Lixun and
            Cui, Kangning and
            Wu, Wenhao and
            Kim, Jinhee and
            Wang, Yixiao and
            Jiang, Ting and
            Ye, Hancheng and
            Wang, Qinsi and
            Yang, Fan and
            Zhuo, Danyang and
            Li, Hai and
            Chen, Yiran},
  title = {{LowRankArena}: A Standardized Evaluation Platform for {SVD}-Based {LLM} Compression},
  booktitle = {The Fortieth Annual Conference on Neural Information Processing Systems},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/139091},
  note = {Accepted conference paper, Evaluations and Datasets Track},
}
Details
LowRankArena overview: evaluation challenges, a standardized SVD-compression platform, and findings on accuracy and inference efficiency

Abstract

SVD-based low-rank compression has become a fast-growing direction for reducing the memory and computational cost of large language models (LLMs). However, meaningful comparison across existing studies remains difficult as prior evaluations use varied benchmarks, inconsistent ratios, and diverse setups, often failing to isolate low-rank effects from auxiliary techniques. As a result, it remains unclear whether reported gains reflect method-level improvements or differences in evaluation protocol. This lack of comparability highlights the need for a unified, reproducible evaluation platform. To address this problem, we present LowRankArena, a standardized evaluation platform for SVD-based LLM compression. LowRankArena unifies task versions, uniform-precision compression budgets, comparison regimes, and inference measurements, and provides a reproducible pipeline with over 3 TiB released compressed checkpoints. Using LowRankArena, our aligned audit of five representative SVD methods reveals that prior findings are highly conditional under standardized protocols: clear leaders and performance tiers shift across backbones and keep ratios, multiple-choice accuracy can hide large perplexity degradation, and nominal low-rank savings yield workload-dependent and often limited end-to-end speedups. Our code is available at: https://github.com/Zishan-Shao/lowrankarena.git.

Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons[NeurIPS'26, Poster]

Wei Tang, Jinpei Han, Kangning Cui, Mattia Carletti, Fredrik K. Gustafsson, Shreyank N Gowda, Patitapaban Palo, Anshul Thakur, Lei Clifton, Jean-Michel Morel, Raymond H. Chan, David A. Clifton, and Xiao Gu

Paper Preprint PDF (preprint)
BibTeX
@inproceedings{tang2026extending,
  author = {Tang, Wei and
            Han, Jinpei and
            Cui, Kangning and
            Carletti, Mattia and
            Gustafsson, Fredrik K. and
            Gowda, Shreyank and
            Palo, Patitapaban and
            Thakur, Anshul and
            Clifton, Lei and
            Morel, Jean-michel and
            Chan, Raymond H and
            Clifton, David and
            Gu, Xiao},
  title = {Extending Pretrained 10-Second {ECG} Foundation Models to Longer Horizons},
  booktitle = {The Fortieth Annual Conference on Neural Information Processing Systems},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150095},
  note = {Accepted conference paper},
}
Details
Overview of extending frozen 10-second ECG foundation models to longer recordings with a lightweight plug-in module

Abstract

Electrocardiogram (ECG) foundation models pretrained on typical diagnostic 10-second ECG segments, have demonstrated strong transferability across a range of clinical applications. However, many real-world applications produce recordings that are typically longer, and are varied in duration during inference time. These 10-second models have no built-in way to combine information across time. Extending them to longer horizons introduces two challenges: structural incompatibilities arising from input-length disparities, and semantic challenges that limit meaningful temporal aggregation. We propose a parameter-efficient framework that extends pretrained ECG foundation models to longer and variable-length ECGs without retraining the backbone. Guided by a frozen pretrained 10-second model, we introduce a lightweight plug-in module that extends the model in two complementary ways: (i) structurally compatible long-sequence processing and (ii) semantically informed temporal modeling. Experiments on multiple long-horizon ECG tasks, datasets, and foundation model backbones demonstrate that our method enables robust long-horizon extension from pretrained snapshot models, consistently outperforming sliding-window and pooling-based baselines with strong parameter efficiency.

Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models[EMNLP'26, Findings]

Zhen Yang†, Sizai Hou†, Kaiwen Zheng, Yaofang Liu, Liang He, Yixuan Chen*, and Kangning Cui*

Paper PDF Preprint Code
BibTeX
@inproceedings{yang-etal-2026-target,
  author = {Yang, Zhen and
            Hou, Sizai and
            Zheng, Kaiwen and
            Liu, Yaofang and
            He, Liang and
            Chen, Yixuan and
            Cui, Kangning},
  title = {Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models},
  booktitle = {Findings of the Association for Computational Linguistics: {EMNLP} 2026},
  editor = {Lee, Hung-Yi and
            Dev, Sunipa and
            Li, Jessy and
            Elliott, Desmond},
  year = {2026},
  pages = {14073--14093},
  publisher = {Association for Computational Linguistics},
  address = {Budapest, Hungary},
  month = {October},
  isbn = {979-8-89176-462-0},
  doi = {10.18653/2026.findings-emnlp.670},
  url = {https://aclanthology.org/2026.findings-emnlp.670/},
}
Details
DPQ uncertainty-preservation results for quantized language models

Abstract

Post-training quantization can preserve model utility while distorting uncertainty and answerability behavior. This work introduces Doubt-Preserving Quantization (DPQ), a target-aware calibration-data selection strategy that chooses calibration strings before GPTQ- or AWQ-style quantization to better preserve the uncertainty behavior of the full-precision model. DPQ changes only the calibration data, leaves the quantizer kernel unchanged, and adds no inference-time overhead. Experiments compare distributional preservation across quantized language models and calibration strategies.

DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions[ICML'26, Spotlight]

Zishan Shao, Lixun Zhang, Kangning Cui, Yixiao Wang, Ting Jiang, Hancheng Ye, Qinsi Wang, Zhixu Du, Yuzhe Fu, Fan Yang, Danyang Zhuo, Yiran Chen, and Hai Helen Li

Top 2.2% Paper PDF Preprint Project Page Code
BibTeX
@inproceedings{pmlr-v306-shao26j,
  author = {Shao, Zishan and
            Zhang, Lixun and
            Cui, Kangning and
            Wang, Yixiao and
            Jiang, Ting and
            Ye, Hancheng and
            Wang, Qinsi and
            Du, Zhixu and
            Fu, Yuzhe and
            Yang, Fan and
            Zhuo, Danyang and
            Chen, Yiran and
            Li, Hai Helen},
  title = {{DecodeShare}: Tracing the Shared Subspace of {LLM} Decode-Time Decisions},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
  editor = {Zhang, Tong and
            Dudik, Miroslav and
            Jaggi, Martin and
            Agarwal, Alekh and
            Li, Sharon and
            Schuurmans, Dale and
            Zhu, Jerry and
            Berkenkamp, Felix and
            Dong, Hanze and
            Bietti, Alberto},
  year = {2026},
  volume = {306},
  pages = {110338--110373},
  series = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
  month = {July},
  url = {https://proceedings.mlr.press/v306/shao26j.html},
}
Details
DecodeShare pipeline: collect cross-task decoding activations, estimate their shared subspace, and analyze explained variance

Abstract

Large language models (LLMs) handle many tasks with one set of parameters, but under KV-cached inference it is unclear what task-general structure, if any, is used at decode time rather than during prefill. We propose DECODESHARE, a protocol that identifies a low-dimensional subspace that is consistently shared across tasks in decode-time hidden states, and then tests its causal role by removing that subspace only during decoding. In our experiments, disturbing the discovered shared subspace degrades decision performance far more than disturbing either a prefill-derived subspace or a random subspace under the same intervention budget. We further find that this decode-shared subspace overlaps common steering vectors, enabling a simple offline adjustment: projecting steering vectors away from the shared subspace can reduce template sensitivity while preserving non-random task utility, with task-dependent trade-offs. Despite being compact, the shared subspace can serve as a high-leverage causal channel at decode time.

Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection[ICMR'26]

Yangchen Zeng†, Zhenyu Yu†, Dongming Jiang, Wenbo Zhang, Yifan Hong, Zhanhua Hu, Jiao Luo, and Kangning Cui*

Paper PDF (preprint) Preprint Code
BibTeX
@inproceedings{zeng2026helpicmr,
  author = {Zeng, Yangchen and
            Yu, Zhenyu and
            Jiang, Dongming and
            Zhang, Wenbo and
            Hong, Yifan and
            Hu, Zhanhua and
            Luo, Jiao and
            Cui, Kangning},
  title = {Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection},
  booktitle = {Proceedings of the 2026 International Conference on Multimedia Retrieval},
  year = {2026},
  pages = {1260--1269},
  publisher = {ACM},
  month = {June},
  doi = {10.1145/3805622.3810593},
  url = {https://doi.org/10.1145/3805622.3810593},
}
Details
Figure for Learning Where to Embed

Abstract

Transformer-based detectors have advanced small-object detection, but they often remain inefficient and vulnerable to background-induced query noise. This work presents HELP, a noise-aware positional-semantic fusion framework that studies where to embed positional information by preserving positional cues in foreground-salient regions while suppressing background clutter. It introduces Heatmap-guided Positional Embedding for both encoder and decoder stages, combines it with a gradient-based mask filter for cleaner query retrieval, and integrates Linear-Snake Convolution to enrich retrieval-relevant representations. The result is a lighter detector that reduces decoder depth and parameters substantially while maintaining competitive accuracy under a reduced compute budget.

Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos[MICCAI'25, Oral]

Yingyu Yang, Qianye Yang, Kangning Cui, Can Peng, Elena D'Alberti, Netzahualcoyotl Hernandez-Cruz, Olga Patey, Aris Papageorghiou, and Alison Noble

Top 2.1% Paper PDF (preprint) Preprint Code
BibTeX
@inproceedings{10.1007/978-3-032-05185-1_31,
  author = {Yang, Yingyu and
            Yang, Qianye and
            Cui, Kangning and
            Peng, Can and
            D'Alberti, Elena and
            Hernandez-Cruz, Netzahualcoyotl and
            Patey, Olga and
            Papageorghiou, Aris T. and
            Noble, J. Alison},
  title = {Latent Motion Profiling for Annotation-Free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos},
  booktitle = {Medical Image Computing and Computer Assisted Intervention -- {MICCAI} 2025},
  editor = {Gee, James C. and
            Alexander, Daniel C. and
            Hong, Jaesung and
            Iglesias, Juan Eugenio and
            Sudre, Carole H. and
            Venkataraman, Archana and
            Golland, Polina and
            Kim, Jong Hyo and
            Park, Jinah},
  year = {2026},
  pages = {316--325},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  isbn = {978-3-032-05185-1},
  doi = {10.1007/978-3-032-05185-1_31},
  url = {https://doi.org/10.1007/978-3-032-05185-1_31},
}
Details
Cardiac motion profiling: an ultrasound reconstruction model separates static structure from motion and identifies end-diastole and end-systole along a latent trajectory

Abstract

The identification of cardiac phase is an essential step for analysis and diagnosis of cardiac function. Automatic methods, especially data-driven methods for cardiac phase detection, typically require extensive annotations, which is time-consuming and labour-intensive. In this paper, we present an unsupervised framework for end-diastole (ED) and end-systole (ES) detection through self-supervised learning of latent cardiac motion trajectories from 4-chamber-view echocardiography videos. Our method eliminates the need for manual annotations, including ED and ES indices, segmentation, or volumetric measurements, by training a reconstruction model to encode interpretable spatiotemporal motion patterns. Evaluated on the EchoNet-Dynamic benchmark, the approach achieves mean absolute error (MAE) of 3.0 frames (58.3 ms) for ED and 2.0 frames (38.8 ms) for ES detection, matching state-of-the-art supervised methods. Extended to fetal echocardiography, the model demonstrates robust performance with MAE 1.5 frames (20.7 ms) for ED and 1.7 frames (25.3 ms) for ES, despite the fact that the fetal heart model is built using non-standardized heart views due to fetal heart positioning variability. Our results demonstrate the potential of the proposed latent motion trajectory strategy for cardiac phase detection in adult and fetal echocardiography. This work advances unsupervised cardiac motion analysis, offering a scalable solution for clinical populations lacking annotated data. Code is released at https://github.com/YingyuYyy/CardiacPhase.

Detection and Geographic Localization of Natural Objects in the Wild: A Case Study on Palms[IJCAI'25, Oral]

Kangning Cui†,*, Rongkun Zhu†, Manqi Wang, Wei Tang, Gregory D. Larsen, Victor P. Pauca, Sarra Alqahtani, Fan Yang, David Segurado, David Lutz, Jean-Michel Morel, and Miles R. Silman

Paper PDF Preprint Poster Slides Code
BibTeX
@inproceedings{ijcai2025p1067,
  author = {Cui, Kangning and
            Zhu, Rongkun and
            Wang, Manqi and
            Tang, Wei and
            Larsen, Gregory D. and
            Pauca, Victor P. and
            Alqahtani, Sarra and
            Yang, Fan and
            Segurado, David and
            Lutz, David A. and
            Morel, Jean-Michel and
            Silman, Miles R.},
  title = {Detection and Geographic Localization of Natural Objects in the Wild: A Case Study on Palms},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, {IJCAI}-25},
  editor = {Kwok, James},
  year = {2025},
  pages = {9601--9609},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  month = {August},
  doi = {10.24963/ijcai.2025/1067},
  url = {https://doi.org/10.24963/ijcai.2025/1067},
  note = {AI and Social Good},
}
Details
PRISM workflow: aerial forest imagery, palm detection, segmentation, and geographic mapping

Abstract

Palms are ecologically and economically indicators of tropical forest health, biodiversity, and human impact that support local economies and global forest product supply chains. While palm detection in plantations is well-studied, efforts to map naturally occurring palms in dense forests remain limited by overlapping crowns, uneven shading, and heterogeneous landscapes. We develop PRISM (Processing, Inference, Segmentation, and Mapping), a flexible pipeline for detecting and localizing palms in dense tropical forests using large orthomosaic images. Orthomosaics are created from thousands of aerial images and spanning several to hundreds of gigabytes. Our contributions are threefold. First, we construct a large UAV-derived orthomosaic dataset collected across 21 ecologically diverse sites in western Ecuador, annotated with 8,830 bounding boxes and 5,026 palm center points. Second, we evaluate multiple state-of-the-art object detectors based on efficiency and performance, integrating zero-shot SAM 2 as the segmentation backbone, and refining the results for precise geographic mapping. Third, we apply calibration methods to align confidence scores with IoU and explore saliency maps for feature explainability. Though optimized for palms, PRISM is adaptable for identifying other natural objects, such as eastern white pines. Future work will explore transfer learning for lower-resolution datasets (0.5–1m). Data and code can be found at github.com/Zippppo/PRISM.

All Publications

Papers, preprints, and conference abstracts, newest first. Years follow the citation record; a conference year may differ. Patents are listed separately.

49 publications and preprints

The Functional Structure of Post-Compression Recovery in Low-Rank LLMs

Zishan Shao, Liang Tian, Georgiy Zemlevskiy, Kangning Cui, Lixun Zhang, Yixiao Wang, Ting Jiang, Jinhee Kim, Yixuan Chen, Rui-Feng Wang, Fan Yang, Hai Li, Yiran Chen

Preprint · 2026Efficient & Reliable MLPreprintPDF
BibTeX
@misc{shao2026functional,
  author = {Shao, Zishan and
            Tian, Liang and
            Zemlevskiy, Georgiy and
            Cui, Kangning and
            Zhang, Lixun and
            Wang, Yixiao and
            Jiang, Ting and
            Kim, Jinhee and
            Chen, Yixuan and
            Wang, Rui-Feng and
            Yang, Fan and
            Li, Hai and
            Chen, Yiran},
  title = {The Functional Structure of Post-Compression Recovery in Low-Rank {LLMs}},
  year = {2026},
  url = {https://arxiv.org/abs/2610.05504v1},
  eprint = {2610.05504},
  archiveprefix = {arXiv},
  version = {1},
  note = {arXiv preprint, version 1},
}
Joint Branch-Space Transform Coding for Diffusion Activation Quantization with Classifier-Free Guidance

Mingrun Jiang, Yuejia Liu, Zishan Shao, Ting Jiang, Qinsi Wang, Hancheng Ye, Yixiao Wang, Rui-Feng Wang, Kangning Cui, Yixuan Chen, Fan Yang, Xiang Cheng, Hai Li, Yiran Chen

Preprint · 2026Efficient & Reliable MLPreprintPDF
BibTeX
@misc{jiang2026joint,
  author = {Jiang, Mingrun and
            Liu, Yuejia and
            Shao, Zishan and
            Jiang, Ting and
            Wang, Qinsi and
            Ye, Hancheng and
            Wang, Yixiao and
            Wang, Rui-Feng and
            Cui, Kangning and
            Chen, Yixuan and
            Yang, Fan and
            Cheng, Xiang and
            Li, Hai and
            Chen, Yiran},
  title = {Joint Branch-Space Transform Coding for Diffusion Activation Quantization with Classifier-Free Guidance},
  year = {2026},
  url = {https://arxiv.org/abs/2610.00930v1},
  eprint = {2610.00930},
  archiveprefix = {arXiv},
  version = {1},
  note = {arXiv preprint, version 1},
}
You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference

Liang He, Jingbo Wen, Yixiong Chen, Yue Yang, Qizhen Lan, Kangning Cui, Xilu Wang

Preprint · 2026Efficient & Reliable MLPreprintPDF
BibTeX
@misc{he2026provider,
  author = {He, Liang and
            Wen, Jingbo and
            Chen, Yixiong and
            Yang, Yue and
            Lan, Qizhen and
            Cui, Kangning and
            Wang, Xilu},
  title = {You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight {LLM} Inference},
  year = {2026},
  url = {https://arxiv.org/abs/2609.37902v1},
  eprint = {2609.37902},
  archiveprefix = {arXiv},
  version = {1},
  note = {arXiv preprint, version 1},
}
LowRankArena: A Standardized Evaluation Platform for SVD-Based LLM Compression

Zishan Shao, Lixun Zhang, Kangning Cui, Wenhao Wu, Jinhee Kim, Yixiao Wang, Ting Jiang, Hancheng Ye, Qinsi Wang, Fan Yang, Danyang Zhuo, Hai Li, Yiran Chen

NeurIPS'26, ED Track, PosterEfficient & Reliable MLPaperPreprintCodePDF (preprint)
BibTeX
@inproceedings{shao2026lowrankarena,
  author = {Shao, Zishan and
            Zhang, Lixun and
            Cui, Kangning and
            Wu, Wenhao and
            Kim, Jinhee and
            Wang, Yixiao and
            Jiang, Ting and
            Ye, Hancheng and
            Wang, Qinsi and
            Yang, Fan and
            Zhuo, Danyang and
            Li, Hai and
            Chen, Yiran},
  title = {{LowRankArena}: A Standardized Evaluation Platform for {SVD}-Based {LLM} Compression},
  booktitle = {The Fortieth Annual Conference on Neural Information Processing Systems},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/139091},
  note = {Accepted conference paper, Evaluations and Datasets Track},
}
Abstract & details
Not All Visual Tokens Are Equally Safe to Remove: Consequence-Sensitive Visual Token Compression

Jingbo Wen, Liang He, Mingyu Cao, Haoyu Wang, Minxuan Hu, Kangning Cui, Xilu Wang

Preprint · 2026Efficient & Reliable MLPreprintPDF
BibTeX
@misc{wen2026consequence,
  author = {Wen, Jingbo and
            He, Liang and
            Cao, Mingyu and
            Wang, Haoyu and
            Hu, Minxuan and
            Cui, Kangning and
            Wang, Xilu},
  title = {Not All Visual Tokens Are Equally Safe to Remove: Consequence-Sensitive Visual Token Compression},
  year = {2026},
  url = {https://arxiv.org/abs/2608.09176v1},
  eprint = {2608.09176},
  archiveprefix = {arXiv},
  version = {1},
  note = {arXiv preprint, version 1},
}
BudgetDraft: Acceptance-Aware Multi-View Training for Sparse-KV Speculative Decoding

Liang He, Jingbo Wen, Qishi Zhan, Yixiong Chen, Kangning Cui, Qizhen Lan, Xilu Wang

Preprint · 2026Efficient & Reliable MLPreprintPDF
BibTeX
@misc{he2026budgetdraft,
  author = {He, Liang and
            Wen, Jingbo and
            Zhan, Qishi and
            Chen, Yixiong and
            Cui, Kangning and
            Lan, Qizhen and
            Wang, Xilu},
  title = {{BudgetDraft}: Acceptance-Aware Multi-View Training for Sparse-{KV} Speculative Decoding},
  year = {2026},
  url = {https://arxiv.org/abs/2606.00144v1},
  eprint = {2606.00144},
  archiveprefix = {arXiv},
  version = {1},
  note = {arXiv preprint, version 1},
}
FlashSVD v1.5: Making Low-Rank Transformers Inference Actually Fast

Wenhao Wu, Zishan Shao, Kangning Cui, Jinhee Kim, Yixiao Wang, Hancheng Ye, Danyang Zhuo, Yiran Chen

Preprint · 2026Efficient & Reliable MLPreprintPDFCode
BibTeX
@misc{wu2026flashsvd,
  author = {Wu, Wenhao and
            Shao, Zishan and
            Cui, Kangning and
            Kim, Jinhee and
            Wang, Yixiao and
            Ye, Hancheng and
            Zhuo, Danyang and
            Chen, Yiran},
  title = {{FlashSVD} v1.5: Making Low-Rank Transformers Inference Actually Fast},
  year = {2026},
  url = {https://arxiv.org/abs/2605.08314v1},
  eprint = {2605.08314},
  archiveprefix = {arXiv},
  version = {1},
  note = {arXiv preprint, version 1},
}
Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models

Zhen Yang, Sizai Hou, Kaiwen Zheng, Yaofang Liu, Liang He, Yixuan Chen, Kangning Cui

EMNLP'26, FindingsEfficient & Reliable MLPaperPreprintCodePDF
BibTeX
@inproceedings{yang-etal-2026-target,
  author = {Yang, Zhen and
            Hou, Sizai and
            Zheng, Kaiwen and
            Liu, Yaofang and
            He, Liang and
            Chen, Yixuan and
            Cui, Kangning},
  title = {Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models},
  booktitle = {Findings of the Association for Computational Linguistics: {EMNLP} 2026},
  editor = {Lee, Hung-Yi and
            Dev, Sunipa and
            Li, Jessy and
            Elliott, Desmond},
  year = {2026},
  pages = {14073--14093},
  publisher = {Association for Computational Linguistics},
  address = {Budapest, Hungary},
  month = {October},
  isbn = {979-8-89176-462-0},
  doi = {10.18653/2026.findings-emnlp.670},
  url = {https://aclanthology.org/2026.findings-emnlp.670/},
}
Abstract & details
From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents

Liang He, Jingbo Wen, Hongyu Gu, Hao Li, Haoyu Wang, Yixiong Chen, Kangning Cui, Xilu Wang

Preprint · 2026Efficient & Reliable MLPreprintPDF
BibTeX
@misc{he2026execution,
  author = {He, Liang and
            Wen, Jingbo and
            Gu, Hongyu and
            Li, Hao and
            Wang, Haoyu and
            Chen, Yixiong and
            Cui, Kangning and
            Wang, Xilu},
  title = {From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based {LLM} Agents},
  year = {2026},
  url = {https://arxiv.org/abs/2608.09168v1},
  eprint = {2608.09168},
  archiveprefix = {arXiv},
  version = {1},
  note = {arXiv preprint, version 1},
}
Auditing Provenance Sensitivity in LLM Agent Action Selection

Junchi Liao, Qiang Huang, Rui Qian, Xutao Mao, Juntao Cheng, Kangning Cui, Yu Li, Jingyu Sun, Yuchao Hou

Preprint · 2026Efficient & Reliable MLPreprintPDF
BibTeX
@misc{liao2026auditing,
  author = {Liao, Junchi and
            Huang, Qiang and
            Qian, Rui and
            Mao, Xutao and
            Cheng, Juntao and
            Cui, Kangning and
            Li, Yu and
            Sun, Jingyu and
            Hou, Yuchao},
  title = {Auditing Provenance Sensitivity in {LLM} Agent Action Selection},
  year = {2026},
  url = {https://arxiv.org/abs/2607.20827v1},
  eprint = {2607.20827},
  archiveprefix = {arXiv},
  version = {1},
  note = {arXiv preprint, version 1},
}
DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions

Zishan Shao, Lixun Zhang, Kangning Cui, Yixiao Wang, Ting Jiang, Hancheng Ye, Qinsi Wang, Zhixu Du, Yuzhe Fu, Fan Yang, Danyang Zhuo, Yiran Chen, Hai Helen Li

ICML'26, SpotlightEfficient & Reliable MLPreprintPaperPDFCode
BibTeX
@inproceedings{pmlr-v306-shao26j,
  author = {Shao, Zishan and
            Zhang, Lixun and
            Cui, Kangning and
            Wang, Yixiao and
            Jiang, Ting and
            Ye, Hancheng and
            Wang, Qinsi and
            Du, Zhixu and
            Fu, Yuzhe and
            Yang, Fan and
            Zhuo, Danyang and
            Chen, Yiran and
            Li, Hai Helen},
  title = {{DecodeShare}: Tracing the Shared Subspace of {LLM} Decode-Time Decisions},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
  editor = {Zhang, Tong and
            Dudik, Miroslav and
            Jaggi, Martin and
            Agarwal, Alekh and
            Li, Sharon and
            Schuurmans, Dale and
            Zhu, Jerry and
            Berkenkamp, Felix and
            Dong, Hanze and
            Bietti, Alberto},
  year = {2026},
  volume = {306},
  pages = {110338--110373},
  series = {Proceedings of Machine Learning Research},
  publisher = {PMLR},
  month = {July},
  url = {https://proceedings.mlr.press/v306/shao26j.html},
}
Abstract & details
Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm

Yaofang Liu, Kangning Cui, Meng Chu, Zhaoqing Li, Suiyun Zhang, Jean-Michel Morel, Xiaodong Cun, Haoxuan Che, Rui Liu, Raymond H. Chan

Preprint · 2026Efficient & Reliable MLPreprintPDFProject
BibTeX
@misc{liu2026visual,
  author = {Liu, Yaofang and
            Cui, Kangning and
            Chu, Meng and
            Li, Zhaoqing and
            Zhang, Suiyun and
            Morel, Jean-Michel and
            Cun, Xiaodong and
            Che, Haoxuan and
            Liu, Rui and
            Chan, Raymond H.},
  title = {Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm},
  year = {2026},
  url = {https://arxiv.org/abs/2605.12271v2},
  eprint = {2605.12271},
  archiveprefix = {arXiv},
  version = {2},
  note = {arXiv preprint, version 2},
}
AwakeForest: An Interactive Geospatial Platform for Large-Scale Forest Imagery

Suraj Prasai, Kangning Cui, Rongkun Zhu, Sarra Alqahtani, Ying Zhang, Victor Paúl Pauca, Miles R. Silman, Fan Yang

IJCAI Demo'26Geospatial AIPreprintPaperPDF
BibTeX
@inproceedings{ijcai2026p986,
  author = {Prasai, Suraj and
            Cui, Kangning and
            Zhu, Rongkun and
            Alqahtani, Sarra and
            Zhang, Ying and
            Pauca, Victor Pa{\'u}l and
            Silman, Miles R. and
            Yang, Fan},
  title = {{AwakeForest}: An Interactive Geospatial Platform for Large-Scale Forest Imagery},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, {IJCAI}-26},
  editor = {Calvanese, Diego},
  year = {2026},
  pages = {8487--8491},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  month = {August},
  doi = {10.24963/ijcai.2026/986},
  url = {https://doi.org/10.24963/ijcai.2026/986},
  note = {Demo Track},
}
GeoTALOS: A Geoscience Platform for Automated Landscape Observation and Spatial Analysis

Suraj Prasai, Kangning Cui, Temidayo Damoye, Rongkun Zhu, Zixiao Luo, David Lutz, Evan Dethier, David Segurado, Ying Zhang, Miles Silman, Victor P. Pauca, Fan Yang

SIGSPATIAL'26, Applications Track · AcceptedGeospatial AIConference record
BibTeX
@inproceedings{prasai2026geotalos,
  author = {Prasai, Suraj and
            Cui, Kangning and
            Damoye, Temidayo and
            Zhu, Rongkun and
            Luo, Zixiao and
            Lutz, David and
            Dethier, Evan and
            Segurado, David and
            Zhang, Ying and
            Silman, Miles and
            Pauca, Victor P. and
            Yang, Fan},
  title = {{GeoTALOS}: A Geoscience Platform for Automated Landscape Observation and Spatial Analysis},
  booktitle = {34th ACM International Conference on Advances in Geographic Information Systems},
  year = {2026},
  url = {https://sigspatial2026.sigspatial.org/application-accepted/},
  note = {Accepted conference paper, Applications Track},
}
ELDOR: A Dataset and Benchmark for Illegal Gold Mining in the Amazon Rainforest

Kangning Cui, Surendra Bohara, Suraj Prasai, Zishan Shao, Wei Tang, Martin Pillaca, Edwin Flores, Zhen Yang, Gregory Larsen, Evan Dethier, David Lutz, Jean-Michel Morel, Miles Silman, Victor Pauca, Fan Yang

Preprint · 2026Geospatial AIPreprintPDF
BibTeX
@misc{cui2026eldor,
  author = {Cui, Kangning and
            Bohara, Surendra and
            Prasai, Suraj and
            Shao, Zishan and
            Tang, Wei and
            Pillaca, Martin and
            Flores, Edwin and
            Yang, Zhen and
            Larsen, Gregory and
            Dethier, Evan and
            Lutz, David and
            Morel, Jean-Michel and
            Silman, Miles and
            Pauca, Victor and
            Yang, Fan},
  title = {{ELDOR}: A Dataset and Benchmark for Illegal Gold Mining in the {Amazon} Rainforest},
  year = {2026},
  url = {https://arxiv.org/abs/2605.15397v1},
  eprint = {2605.15397},
  archiveprefix = {arXiv},
  version = {1},
  note = {arXiv preprint, version 1},
}
From Orthomosaics to Raw UAV Imagery: Enhancing Palm Detection and Crown-Center Localization

Rongkun Zhu, Kangning Cui, Wei Tang, Rui-Feng Wang, Sarra Alqahtani, David Lutz, Fan Yang, Paul Fine, Jordan Karubian, Robert Plemmons, Jean-Michel Morel, Victor Pauca, Miles Silman

IGARSS'26Geospatial AIPreprintPaperPDF (preprint)
BibTeX
@inproceedings{zhu2026rawuav,
  author = {Zhu, Rongkun and
            Cui, Kangning and
            Tang, Wei and
            Wang, Rui-Feng and
            Alqahtani, Sarra and
            Lutz, David and
            Yang, Fan and
            Fine, Paul and
            Karubian, Jordan and
            Plemmons, Robert and
            Morel, Jean-Michel and
            Pauca, Victor and
            Silman, Miles},
  title = {From Orthomosaics to Raw {UAV} Imagery: Enhancing Palm Detection and Crown-Center Localization},
  booktitle = {{IGARSS} 2026 - 2026 {IEEE} International Geoscience and Remote Sensing Symposium},
  year = {2026},
  pages = {3390--3394},
  publisher = {IEEE},
  month = {August},
  doi = {10.1109/igarss60577.2026.11731064},
  url = {https://doi.org/10.1109/igarss60577.2026.11731064},
}
Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection

Yangchen Zeng, Zhenyu Yu, Dongming Jiang, Wenbo Zhang, Yifan Hong, Zhanhua Hu, Jiao Luo, Kangning Cui

ICMR'26Geospatial AIPaperPreprintCodePDF (preprint)
BibTeX
@inproceedings{zeng2026helpicmr,
  author = {Zeng, Yangchen and
            Yu, Zhenyu and
            Jiang, Dongming and
            Zhang, Wenbo and
            Hong, Yifan and
            Hu, Zhanhua and
            Luo, Jiao and
            Cui, Kangning},
  title = {Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection},
  booktitle = {Proceedings of the 2026 International Conference on Multimedia Retrieval},
  year = {2026},
  pages = {1260--1269},
  publisher = {ACM},
  month = {June},
  doi = {10.1145/3805622.3810593},
  url = {https://doi.org/10.1145/3805622.3810593},
}
Abstract & details
Center-guided Classifier for Semantic Segmentation of Remote Sensing Images

Wei Zhang, Mengting Ma, Yizhen Jiang, Yun Chen, Zhenhua Huang, Wangyu Wu, Kangning Cui, Rongrong Lian, Zhenkai Wu, Xiaowen Ma

TGRS'26Geospatial AIPaperPreprintPDF (preprint)
BibTeX
@article{zhang2026centerseg,
  author = {Zhang, Wei and
            Ma, Mengting and
            Jiang, Yizhen and
            Chen, Yun and
            Huang, Zhenhua and
            Wu, Wangyu and
            Cui, Kangning and
            Lian, Rongrong and
            Wu, Zhenkai and
            Ma, Xiaowen},
  title = {Center-Guided Classifier for Semantic Segmentation of Remote Sensing Images},
  journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
  year = {2026},
  volume = {64},
  pages = {1--16},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  doi = {10.1109/tgrs.2026.3657453},
  url = {https://doi.org/10.1109/tgrs.2026.3657453},
}
Abstract & details
Superpixel-Level Hypergraph Regularized Multi-View Clustering for Hyperspectral Images

Shujun Yang, Kangning Cui, Yuheng Jia, Yao Ding, Renxiang Guan, Yun Ding

JSTARS'26Geospatial AIPaper
BibTeX
@article{yang2026hypergraph,
  author = {Yang, Shujun and
            Cui, Kangning and
            Jia, Yuheng and
            Ding, Yao and
            Guan, Renxiang and
            Ding, Yun},
  title = {Superpixel-Level Hypergraph Regularized Multiview Clustering for Hyperspectral Images},
  journal = {{IEEE} Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  volume = {19},
  pages = {10473--10488},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  doi = {10.1109/jstars.2026.3673839},
  url = {https://doi.org/10.1109/jstars.2026.3673839},
}
Advanced Remote Sensing and AI Techniques in Agriculture and Forestry

Rui-Feng Wang, Kangning Cui

Plants'26Geospatial AIPaper
BibTeX
@article{wang2026agriculture,
  author = {Wang, Rui-Feng and
            Cui, Kangning},
  title = {Advanced Remote Sensing and {AI} Techniques in Agriculture and Forestry},
  journal = {Plants},
  year = {2026},
  volume = {15},
  number = {17},
  pages = {2706},
  publisher = {MDPI AG},
  month = {September},
  doi = {10.3390/plants15172706},
  url = {https://doi.org/10.3390/plants15172706},
}
JassidNet as a quantization-aware lightweight phenotyping framework for high-throughput cotton jassid (Amrasca biguttula) detection and counting toward objective resistance screening

Rui-Feng Wang, Kangning Cui, Iago Beffart Schardong, Matthew C. Bauer, Rama Vamsi Somala, Mingrui Xu, Dalton West, Donald C. Jones, Sally V. Taylor, Phillip M. Roberts, Changying Li, Peng W. Chee

Industrial Crops and Products'26Geospatial AIPaperCode
BibTeX
@article{wang2026jassid,
  author = {Wang, Rui-Feng and
            Cui, Kangning and
            Schardong, Iago Beffart and
            Bauer, Matthew C. and
            Somala, Rama Vamsi and
            Xu, Mingrui and
            West, Dalton and
            Jones, Donald C. and
            Taylor, Sally V. and
            Roberts, Phillip M. and
            Li, Changying and
            Chee, Peng W.},
  title = {{JassidNet} as a quantization-aware lightweight phenotyping framework for high-throughput cotton jassid ({Amrasca} biguttula) detection and counting toward objective resistance screening},
  journal = {Industrial Crops and Products},
  year = {2026},
  volume = {249},
  pages = {123716},
  publisher = {Elsevier BV},
  month = {July},
  doi = {10.1016/j.indcrop.2026.123716},
  url = {https://doi.org/10.1016/j.indcrop.2026.123716},
}
BioMoth: Non-Lethal Estimation of Moth Abundance and Biomass from Blacklight Sheet Imagery Using Computer Vision

David A. Lutz, Suhavi Suhavi, Jessica S. Jones, Quinn M. Aldrich, Wyatt J. Cummings, Kangning Cui, Matthew P. Ayres

Preprint · 2026Geospatial AIPreprint
BibTeX
@misc{lutz2026biomoth,
  author = {Lutz, David A. and
            Suhavi, Suhavi and
            Jones, Jessica S. and
            Aldrich, Quinn M. and
            Cummings, Wyatt J. and
            Cui, Kangning and
            Ayres, Matthew P.},
  title = {{BioMoth}: Non-Lethal Estimation of Moth Abundance and Biomass from Blacklight Sheet Imagery Using Computer Vision},
  year = {2026},
  doi = {10.2139/ssrn.6943698},
  url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6943698},
  eprint = {6943698},
  archiveprefix = {SSRN},
  note = {SSRN preprint, posted 15 June 2026},
}
SEAF-Net: A Sustainable and Lightweight Attention-Enhanced Detection Network for Underwater Fish Species Recognition

Yu-Shan Han, Sheng-Lun Zhao, Chu Chen, Kangning Cui, Pingfan Hu, Rui-Feng Wang

JMSE'26Geospatial AIPaper
BibTeX
@article{han2026seaf,
  author = {Han, Yu-Shan and
            Zhao, Sheng-Lun and
            Chen, Chu and
            Cui, Kangning and
            Hu, Pingfan and
            Wang, Rui-Feng},
  title = {{SEAF-Net}: A Sustainable and Lightweight Attention-Enhanced Detection Network for Underwater Fish Species Recognition},
  journal = {Journal of Marine Science and Engineering},
  year = {2026},
  volume = {14},
  number = {4},
  pages = {351},
  publisher = {MDPI AG},
  month = {February},
  doi = {10.3390/jmse14040351},
  url = {https://doi.org/10.3390/jmse14040351},
}
Cott-ADNet: Lightweight Real-Time Cotton Boll and Flower Detection Under Field Conditions

Rui-Feng Wang, Mingrui Xu, Matthew Bauer, Iago Schardong, Xiaowen Ma, Peng Chee, Kangning Cui

WACV'26 WorkshopGeospatial AIPaperPDFCode & data
BibTeX
@inproceedings{Wang_2026_WACV,
  author = {Wang, Rui-Feng and
            Xu, Mingrui and
            Bauer, Matthew and
            Schardong, Iago and
            Ma, Xiaowen and
            Chee, Peng and
            Cui, Kangning},
  title = {{Cott-ADNet}: Lightweight Real-Time Cotton Boll and Flower Detection Under Field Conditions},
  booktitle = {Proceedings of the {IEEE}/{CVF} Winter Conference on Applications of Computer Vision ({WACV}) Workshops},
  year = {2026},
  pages = {500--509},
  month = {March},
  url = {https://openaccess.thecvf.com/content/WACV2026W/HARVEST-Vision/html/Wang_Cott-ADNet_Lightweight_Real-Time_Cotton_Boll_and_Flower_Detection_Under_Field_WACVW_2026_paper.html},
}
Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons

Wei Tang, Jinpei Han, Kangning Cui, Mattia Carletti, Fredrik K. Gustafsson, Shreyank Gowda, Patitapaban Palo, Anshul Thakur, Lei Clifton, Jean-michel Morel, Raymond H Chan, David Clifton, Xiao Gu

NeurIPS'26, PosterMedical AIPaperPreprintPDF (preprint)
BibTeX
@inproceedings{tang2026extending,
  author = {Tang, Wei and
            Han, Jinpei and
            Cui, Kangning and
            Carletti, Mattia and
            Gustafsson, Fredrik K. and
            Gowda, Shreyank and
            Palo, Patitapaban and
            Thakur, Anshul and
            Clifton, Lei and
            Morel, Jean-michel and
            Chan, Raymond H and
            Clifton, David and
            Gu, Xiao},
  title = {Extending Pretrained 10-Second {ECG} Foundation Models to Longer Horizons},
  booktitle = {The Fortieth Annual Conference on Neural Information Processing Systems},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/150095},
  note = {Accepted conference paper},
}
Abstract & details
Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos

Yingyu Yang, Qianye Yang, Kangning Cui, Can Peng, Elena D'Alberti, Netzahualcoyotl Hernandez-Cruz, Olga Patey, Aris T. Papageorghiou, J. Alison Noble

MICCAI'25, Oral · Published 2026Medical AIPaperCodePDF (preprint)
BibTeX
@inproceedings{10.1007/978-3-032-05185-1_31,
  author = {Yang, Yingyu and
            Yang, Qianye and
            Cui, Kangning and
            Peng, Can and
            D'Alberti, Elena and
            Hernandez-Cruz, Netzahualcoyotl and
            Patey, Olga and
            Papageorghiou, Aris T. and
            Noble, J. Alison},
  title = {Latent Motion Profiling for Annotation-Free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos},
  booktitle = {Medical Image Computing and Computer Assisted Intervention -- {MICCAI} 2025},
  editor = {Gee, James C. and
            Alexander, Daniel C. and
            Hong, Jaesung and
            Iglesias, Juan Eugenio and
            Sudre, Carole H. and
            Venkataraman, Archana and
            Golland, Polina and
            Kim, Jong Hyo and
            Park, Jinah},
  year = {2026},
  pages = {316--325},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  isbn = {978-3-032-05185-1},
  doi = {10.1007/978-3-032-05185-1_31},
  url = {https://doi.org/10.1007/978-3-032-05185-1_31},
}
Abstract & details
Blind Restoration of High-Resolution Ultrasound Video

Chu Chen, Kangning Cui, Pasquale Cascarano, Wei Tang, Elena Loli Piccolomini, Raymond H. Chan

MICCAI'25 · Published 2026Medical AIPaper
BibTeX
@inproceedings{10.1007/978-3-032-04947-6_8,
  author = {Chen, Chu and
            Cui, Kangning and
            Cascarano, Pasquale and
            Tang, Wei and
            Piccolomini, Elena Loli and
            Chan, Raymond H.},
  title = {Blind Restoration of High-Resolution Ultrasound Video},
  booktitle = {Medical Image Computing and Computer Assisted Intervention -- {MICCAI} 2025},
  editor = {Gee, James C. and
            Alexander, Daniel C. and
            Hong, Jaesung and
            Iglesias, Juan Eugenio and
            Sudre, Carole H. and
            Venkataraman, Archana and
            Golland, Polina and
            Kim, Jong Hyo and
            Park, Jinah},
  year = {2026},
  pages = {77--87},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  isbn = {978-3-032-04947-6},
  doi = {10.1007/978-3-032-04947-6_8},
  url = {https://doi.org/10.1007/978-3-032-04947-6_8},
}
A Local Perspective-based Model for Overlapping Community Detection

Gaofeng Zhou, Rui-Feng Wang, Kangning Cui

IEEE SMC'25Efficient & Reliable MLPaperPreprint
BibTeX
@inproceedings{zhou2025community,
  author = {Zhou, Gaofeng and
            Wang, Rui-Feng and
            Cui, Kangning},
  title = {A Local Perspective-based Model for Overlapping Community Detection},
  booktitle = {2025 {IEEE} International Conference on Systems, Man, and Cybernetics ({SMC})},
  year = {2025},
  pages = {2965--2970},
  publisher = {IEEE},
  month = {October},
  doi = {10.1109/smc58881.2025.11343612},
  url = {https://doi.org/10.1109/smc58881.2025.11343612},
}
Efficient Localization and Spatial Distribution Modeling of Canopy Palms Using UAV Imagery

Kangning Cui, Wei Tang, Rongkun Zhu, Manqi Wang, Gregory D. Larsen, Victor P. Pauca, Sarra Alqahtani, Fan Yang, David Segurado, Paul Fine, Jordan Karubian, Raymond H. Chan, Robert J. Plemmons, Jean-Michel Morel, Miles R. Silman

TGRS'25Geospatial AIPaperPreprintDatasetCodePDF (preprint)
BibTeX
@article{cui2025palmstgrs,
  author = {Cui, Kangning and
            Tang, Wei and
            Zhu, Rongkun and
            Wang, Manqi and
            Larsen, Gregory D. and
            Pauca, Victor P. and
            Alqahtani, Sarra and
            Yang, Fan and
            Segurado, David and
            Fine, Paul and
            Karubian, Jordan and
            Chan, Raymond H. and
            Plemmons, Robert J. and
            Morel, Jean-Michel and
            Silman, Miles R.},
  title = {Efficient Localization and Spatial Distribution Modeling of Canopy Palms Using {UAV} Imagery},
  journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
  year = {2025},
  volume = {63},
  pages = {1--15},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  doi = {10.1109/tgrs.2025.3584093},
  url = {https://doi.org/10.1109/tgrs.2025.3584093},
}
Abstract & details
Detection and Geographic Localization of Natural Objects in the Wild: A Case Study on Palms

Kangning Cui, Rongkun Zhu, Manqi Wang, Wei Tang, Gregory D. Larsen, Victor P. Pauca, Sarra Alqahtani, Fan Yang, David Segurado, David A. Lutz, Jean-Michel Morel, Miles R. Silman

IJCAI'25, OralGeospatial AIPaperCodePDF
BibTeX
@inproceedings{ijcai2025p1067,
  author = {Cui, Kangning and
            Zhu, Rongkun and
            Wang, Manqi and
            Tang, Wei and
            Larsen, Gregory D. and
            Pauca, Victor P. and
            Alqahtani, Sarra and
            Yang, Fan and
            Segurado, David and
            Lutz, David A. and
            Morel, Jean-Michel and
            Silman, Miles R.},
  title = {Detection and Geographic Localization of Natural Objects in the Wild: A Case Study on Palms},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, {IJCAI}-25},
  editor = {Kwok, James},
  year = {2025},
  pages = {9601--9609},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  month = {August},
  doi = {10.24963/ijcai.2025/1067},
  url = {https://doi.org/10.24963/ijcai.2025/1067},
  note = {AI and Social Good},
}
Abstract & details
Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion

Xingyu Di, Kangning Cui, Rui-Feng Wang

Remote Sensing'25Geospatial AIPaper
BibTeX
@article{di2025uav,
  author = {Di, Xingyu and
            Cui, Kangning and
            Wang, Rui-Feng},
  title = {Toward Efficient {UAV}-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion},
  journal = {Remote Sensing},
  year = {2025},
  volume = {17},
  number = {13},
  pages = {2235},
  publisher = {MDPI AG},
  month = {June},
  doi = {10.3390/rs17132235},
  url = {https://doi.org/10.3390/rs17132235},
}
MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing

Debashis Gupta, Aditi Golder, Rongkhun Zhu, Kangning Cui, Wei Tang, Fan Yang, Ovidiu Csillik, Sarra Alaqahtani, V. Paul Pauca

Preprint · 2025Geospatial AIPreprintPDF
BibTeX
@misc{gupta2025mosaic,
  author = {Gupta, Debashis and
            Golder, Aditi and
            Zhu, Rongkhun and
            Cui, Kangning and
            Tang, Wei and
            Yang, Fan and
            Csillik, Ovidiu and
            Alaqahtani, Sarra and
            Pauca, V. Paul},
  title = {{MoSAiC}: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing},
  year = {2025},
  url = {https://arxiv.org/abs/2507.08683v1},
  eprint = {2507.08683},
  archiveprefix = {arXiv},
  version = {1},
  note = {arXiv preprint, version 1},
}
RA-CottNet: A Real-Time High-Precision Deep Learning Model for Cotton Boll and Flower Recognition

Rui-Feng Wang, Yi-Ming Qin, Yi-Yi Zhao, Mingrui Xu, Iago Beffart Schardong, Kangning Cui

AI'25Geospatial AIPaper
BibTeX
@article{wang2025racott,
  author = {Wang, Rui-Feng and
            Qin, Yi-Ming and
            Zhao, Yi-Yi and
            Xu, Mingrui and
            Schardong, Iago Beffart and
            Cui, Kangning},
  title = {{RA-CottNet}: A Real-Time High-Precision Deep Learning Model for Cotton Boll and Flower Recognition},
  journal = {{AI}},
  year = {2025},
  volume = {6},
  number = {9},
  pages = {235},
  publisher = {MDPI AG},
  month = {September},
  doi = {10.3390/ai6090235},
  url = {https://doi.org/10.3390/ai6090235},
}
Resource-Efficient Cotton Network: A Lightweight Deep Learning Framework for Cotton Disease and Pest Classification

Zhengle Wang, Heng-Wei Zhang, Ying-Qiang Dai, Kangning Cui, Haihua Wang, Peng W. Chee, Rui-Feng Wang

Plants'25Geospatial AIPaper
BibTeX
@article{wang2025cotton,
  author = {Wang, Zhengle and
            Zhang, Heng-Wei and
            Dai, Ying-Qiang and
            Cui, Kangning and
            Wang, Haihua and
            Chee, Peng W. and
            Wang, Rui-Feng},
  title = {Resource-Efficient Cotton Network: A Lightweight Deep Learning Framework for Cotton Disease and Pest Classification},
  journal = {Plants},
  year = {2025},
  volume = {14},
  number = {13},
  pages = {2082},
  publisher = {MDPI AG},
  month = {July},
  doi = {10.3390/plants14132082},
  url = {https://doi.org/10.3390/plants14132082},
}
A Deep Learning Framework for Fetal Heart Tracking in Ultrasound Videos: Toward Enhanced Congenital Heart Defects Detection

Qianye Yang, Kangning Cui, Yipeng Hu, Can Peng, Netzahualcoyotl Hernandez-Cruz, Rahul Ahuja, Elena D'Alberti, Chan Raymond, Olga Patey, Aris Papageorghiou, J. Alison Noble

FIMH'25Medical AIPaper
BibTeX
@inproceedings{10.1007/978-3-031-94562-5_20,
  author = {Yang, Qianye and
            Cui, Kangning and
            Hu, Yipeng and
            Peng, Can and
            Hernandez-Cruz, Netzahualcoyotl and
            Ahuja, Rahul and
            D'Alberti, Elena and
            Raymond, Chan and
            Patey, Olga and
            Papageorghiou, Aris and
            Noble, J. Alison},
  title = {A Deep Learning Framework for Fetal Heart Tracking in Ultrasound Videos: Toward Enhanced Congenital Heart Defects Detection},
  booktitle = {Functional Imaging and Modeling of the Heart},
  editor = {Chabiniok, Radom{\'i}r and
            Zou, Qing and
            Hussain, Tarique and
            Nguyen, Hoang H. and
            Zaha, Vlad G. and
            Gusseva, Maria},
  year = {2025},
  pages = {219--230},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  isbn = {978-3-031-94562-5},
  doi = {10.1007/978-3-031-94562-5_20},
  url = {https://doi.org/10.1007/978-3-031-94562-5_20},
}
Bilateral Signal Warping for Left Ventricular Hypertrophy Diagnosis

Wei Tang, Kangning Cui, Raymond H. Chan, Jean-Michel Morel

ISBI'25, OralMedical AIPaper
BibTeX
@inproceedings{tang2025bilateralwarping,
  author = {Tang, Wei and
            Cui, Kangning and
            Chan, Raymond H. and
            Morel, Jean-Michel},
  title = {Bilateral Signal Warping for Left Ventricular Hypertrophy Diagnosis},
  booktitle = {2025 {IEEE} 22nd International Symposium on Biomedical Imaging (ISBI)},
  year = {2025},
  pages = {1--5},
  publisher = {IEEE},
  month = {April},
  doi = {10.1109/isbi60581.2025.10980912},
  url = {https://doi.org/10.1109/isbi60581.2025.10980912},
}
PalmProbNet: A Probabilistic Approach to Understanding Palm Distributions in Ecuadorian Tropical Forest via Transfer Learning

Kangning Cui, Zishan Shao, Gregory Larsen, Victor Pauca, Sarra Alqahtani, David Segurado, João Pinheiro, Manqi Wang, David Lutz, Robert Plemmons, Miles Silman

ACMSE'24Geospatial AIPaperPreprintPDF (preprint)
BibTeX
@inproceedings{cui2024palmprobnet,
  author = {Cui, Kangning and
            Shao, Zishan and
            Larsen, Gregory and
            Pauca, Victor and
            Alqahtani, Sarra and
            Segurado, David and
            Pinheiro, Jo{\~a}o and
            Wang, Manqi and
            Lutz, David and
            Plemmons, Robert and
            Silman, Miles},
  title = {{PalmProbNet}: A Probabilistic Approach to Understanding Palm Distributions in {Ecuadorian} Tropical Forest via Transfer Learning},
  booktitle = {Proceedings of the 2024 {ACM} Southeast Conference},
  year = {2024},
  pages = {272--277},
  publisher = {ACM},
  month = {April},
  doi = {10.1145/3603287.3651220},
  url = {https://doi.org/10.1145/3603287.3651220},
}
Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering

Kangning Cui, Ruoning Li, Sam L Polk, Yinyi Lin, Hongsheng Zhang, James M. Murphy, Robert J. Plemmons, Raymond H. Chan

TGRS'24Geospatial AIPaperPreprintCodePDF (preprint)
BibTeX
@article{cui2024s2dl,
  author = {Cui, Kangning and
            Li, Ruoning and
            Polk, Sam L and
            Lin, Yinyi and
            Zhang, Hongsheng and
            Murphy, James M. and
            Plemmons, Robert J. and
            Chan, Raymond H.},
  title = {Superpixel-Based and Spatially Regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering},
  journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
  year = {2024},
  volume = {62},
  pages = {1--18},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  doi = {10.1109/tgrs.2024.3385202},
  url = {https://doi.org/10.1109/tgrs.2024.3385202},
}
Abstract & details
Accurate Detection and Instance Segmentation of Unstained Living Adherent Cells in Differential Interference Contrast Images

Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen, Yanfang Li, Yaofang Liu, Adnan Shakoor, Han Zhao, Beijia Lu, Shaohua Zhi, Raymond Hon-Fu Chan, Dong Sun

CIBM'24Medical AIPaper
BibTeX
@article{pan2024cellsegmentation,
  author = {Pan, Fei and
            Wu, Yutong and
            Cui, Kangning and
            Chen, Shuxun and
            Li, Yanfang and
            Liu, Yaofang and
            Shakoor, Adnan and
            Zhao, Han and
            Lu, Beijia and
            Zhi, Shaohua and
            Chan, Raymond Hon-Fu and
            Sun, Dong},
  title = {Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images},
  journal = {Computers in Biology and Medicine},
  year = {2024},
  volume = {182},
  pages = {109151},
  publisher = {Elsevier BV},
  month = {November},
  doi = {10.1016/j.compbiomed.2024.109151},
  url = {https://doi.org/10.1016/j.compbiomed.2024.109151},
}
Optimized Hard Exudate Detection with Supervised Contrastive Learning

Wei Tang, Kangning Cui, Raymond H. Chan

ISBI'24Medical AIPaper
BibTeX
@inproceedings{tang2024hardexudates,
  author = {Tang, Wei and
            Cui, Kangning and
            Chan, Raymond H.},
  title = {Optimized Hard Exudate Detection with Supervised Contrastive Learning},
  booktitle = {2024 {IEEE} International Symposium on Biomedical Imaging (ISBI)},
  year = {2024},
  pages = {1--5},
  publisher = {IEEE},
  month = {May},
  doi = {10.1109/isbi56570.2024.10635760},
  url = {https://doi.org/10.1109/isbi56570.2024.10635760},
}
Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

Sam L. Polk, Kangning Cui, Aland H. Y. Chan, David A. Coomes, Robert J. Plemmons, James M. Murphy

Remote Sensing'23Geospatial AIPaper
BibTeX
@article{polk2023diffusionvolume,
  author = {Polk, Sam L. and
            Cui, Kangning and
            Chan, Aland H. Y. and
            Coomes, David A. and
            Plemmons, Robert J. and
            Murphy, James M.},
  title = {Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images},
  journal = {Remote Sensing},
  year = {2023},
  volume = {15},
  number = {4},
  pages = {1053},
  publisher = {MDPI AG},
  month = {February},
  doi = {10.3390/rs15041053},
  url = {https://doi.org/10.3390/rs15041053},
}
Change Detection of Amazonian Alluvial Gold Mining Using Deep Learning and Sentinel-2 Imagery

Seda Camalan, Kangning Cui, Victor Paul Pauca, Sarra Alqahtani, Miles Silman, Raymond Chan, Robert Jame Plemmons, Evan Nylen Dethier, Luis E. Fernandez, David A. Lutz

RS'22Geospatial AIPaper
BibTeX
@article{camalan2022goldmining,
  author = {Camalan, Seda and
            Cui, Kangning and
            Pauca, Victor Paul and
            Alqahtani, Sarra and
            Silman, Miles and
            Chan, Raymond and
            Plemmons, Robert Jame and
            Dethier, Evan Nylen and
            Fernandez, Luis E. and
            Lutz, David A.},
  title = {Change Detection of {Amazonian} Alluvial Gold Mining Using Deep Learning and {Sentinel-2} Imagery},
  journal = {Remote Sensing},
  year = {2022},
  volume = {14},
  number = {7},
  pages = {1746},
  publisher = {MDPI AG},
  month = {April},
  doi = {10.3390/rs14071746},
  url = {https://doi.org/10.3390/rs14071746},
}
Semi-Supervised Change Detection Of Small Water Bodies Using RGB And Multispectral Images In Peruvian Rainforests

Kangning Cui, Seda Camalan, Ruoning Li, Victor Paul Pauca, Sarra Alqahtani, Robert Plemmons, Miles Silman, Evan Nylen Dethier, David Lutz, Raymond Chan

WHISPERS'22Geospatial AIPaperPreprintCode
BibTeX
@inproceedings{cui2022water,
  author = {Cui, Kangning and
            Camalan, Seda and
            Li, Ruoning and
            Pauca, Victor Paul and
            Alqahtani, Sarra and
            Plemmons, Robert and
            Silman, Miles and
            Dethier, Evan Nylen and
            Lutz, David and
            Chan, Raymond},
  title = {Semi-Supervised Change Detection Of Small Water Bodies Using {RGB} And Multispectral Images In {Peruvian} Rainforests},
  booktitle = {2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing ({WHISPERS})},
  year = {2022},
  pages = {1--5},
  publisher = {IEEE},
  month = {September},
  doi = {10.1109/whispers56178.2022.9955140},
  url = {https://doi.org/10.1109/whispers56178.2022.9955140},
}
Detecting Change Due to Alluvial Gold Mining in Peruvian Rainforest Using Recursive Convolutional Neural Networks and Contrastive Learning

Seda Camalan, Kangning Cui, Paúl Pauca, Sarra Alqahtani, Miles Silman, Raymond Chan, Evan Dethier, Luis E. Fernandez, David A. Lutz, Robert Plemmons

AGU'22 · Conference abstractGeospatial AIAbstractScholar
BibTeX
@inproceedings{camalan2022detecting,
  author = {Camalan, Seda and
            Cui, Kangning and
            Pauca, Pa{\'u}l and
            Alqahtani, Sarra and
            Silman, Miles and
            Chan, Raymond and
            Dethier, Evan and
            Fernandez, Luis E. and
            Lutz, David A. and
            Plemmons, Robert},
  title = {Detecting Change Due to Alluvial Gold Mining in {Peruvian} Rainforest Using Recursive Convolutional Neural Networks and Contrastive Learning},
  booktitle = {{AGU} Fall Meeting Abstracts},
  year = {2022},
  volume = {2022},
  eid = {B52G-0905},
  month = {December},
  url = {https://ui.adsabs.harvard.edu/abs/2022AGUFM.B52G0905C/abstract},
  note = {Conference abstract B52G-0905},
}
Unsupervised Spatial-Spectral Hyperspectral Image Reconstruction and Clustering With Diffusion Geometry

Kangning Cui, Ruoning Li, Sam L. Polk, James M. Murphy, Robert J. Plemmons, Raymond H. Chan

WHISPERS'22Geospatial AIPreprintPaperPDF (preprint)
BibTeX
@inproceedings{cui2022dsirc,
  author = {Cui, Kangning and
            Li, Ruoning and
            Polk, Sam L. and
            Murphy, James M. and
            Plemmons, Robert J. and
            Chan, Raymond H.},
  title = {Unsupervised Spatial-Spectral Hyperspectral Image Reconstruction And Clustering With Diffusion Geometry},
  booktitle = {2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing ({WHISPERS})},
  year = {2022},
  pages = {1--5},
  publisher = {IEEE},
  month = {September},
  doi = {10.1109/whispers56178.2022.9955069},
  url = {https://doi.org/10.1109/whispers56178.2022.9955069},
}
Unsupervised Detection of Ash Dieback Disease Using Diffusion-Based Hyperspectral Image Clustering

Sam L. Polk, Aland H. Y. Chan, Kangning Cui, Robert J. Plemmons, David A. Coomes, James M. Murphy

IGARSS'22Geospatial AIPaperPreprintPDF (preprint)
BibTeX
@inproceedings{polk2022ashdieback,
  author = {Polk, Sam L. and
            Chan, Aland H. Y. and
            Cui, Kangning and
            Plemmons, Robert J. and
            Coomes, David A. and
            Murphy, James M.},
  title = {Unsupervised Detection of ASH Dieback Disease ({Hymenoscyphus} Fraxineus) Using Diffusion-Based Hyperspectral Image Clustering},
  booktitle = {{IGARSS} 2022 - 2022 {IEEE} International Geoscience and Remote Sensing Symposium},
  year = {2022},
  pages = {2287--2290},
  publisher = {IEEE},
  month = {July},
  doi = {10.1109/igarss46834.2022.9883429},
  url = {https://doi.org/10.1109/igarss46834.2022.9883429},
}
Classification of Hyperspectral Images Using SVM With Shape-Adaptive Reconstruction and Smoothed Total Variation

Ruoning Li, Kangning Cui, Raymond H. Chan, Robert J. Plemmons

IGARSS'22Geospatial AIPaper
BibTeX
@inproceedings{li2022svmsatv,
  author = {Li, Ruoning and
            Cui, Kangning and
            Chan, Raymond H. and
            Plemmons, Robert J.},
  title = {Classification of Hyperspectral Images Using {SVM} with Shape-Adaptive Reconstruction and Smoothed Total Variation},
  booktitle = {{IGARSS} 2022 - 2022 {IEEE} International Geoscience and Remote Sensing Symposium},
  year = {2022},
  pages = {1368--1371},
  publisher = {IEEE},
  month = {July},
  doi = {10.1109/igarss46834.2022.9883961},
  url = {https://doi.org/10.1109/igarss46834.2022.9883961},
}
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images

Sam L. Polk, Kangning Cui, Robert J. Plemmons, James M. Murphy

IGARSS'22Geospatial AIPaperPreprintPDF (preprint)
BibTeX
@inproceedings{polk2022activediffusion,
  author = {Polk, Sam L. and
            Cui, Kangning and
            Plemmons, Robert J. and
            Murphy, James M.},
  title = {Active Diffusion and {VCA}-Assisted Image Segmentation of Hyperspectral Images},
  booktitle = {{IGARSS} 2022 - 2022 {IEEE} International Geoscience and Remote Sensing Symposium},
  year = {2022},
  pages = {1364--1367},
  publisher = {IEEE},
  month = {July},
  doi = {10.1109/igarss46834.2022.9883894},
  url = {https://doi.org/10.1109/igarss46834.2022.9883894},
}
Unsupervised Classification of AVIRIS-NG Hyperspectral Images

Kangning Cui, Robert J. Plemmons

WHISPERS'21Geospatial AIPaper
BibTeX
@inproceedings{cui2021avirisng,
  author = {Cui, Kangning and
            Plemmons, Robert J.},
  title = {Unsupervised Classification of {AVIRIS-NG} Hyperspectral Images},
  booktitle = {2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing ({WHISPERS})},
  year = {2021},
  pages = {1--5},
  publisher = {IEEE},
  month = {March},
  doi = {10.1109/whispers52202.2021.9484006},
  url = {https://doi.org/10.1109/whispers52202.2021.9484006},
}

Research in the Media

Coverage of our collaborators’ work in forest monitoring and conservation, alongside related papers and projects.

Patents