Publications

@inproceedings{hoque_prism-cafo_2026,

title = {PRISM-CAFO: Prior-conditioned Remote-sensing Infrastructure Segmentation and Mapping for CAFOs},

author = {Oishee Bintey Hoque and Nibir Chandra Mandal and Kyle Luong and Amanda Wilson and Samarth Swarup and Madhav Marathe and Abhijin Adiga},

url = {https://ieeexplore.ieee.org/abstract/document/11492693},

doi = {10.1109/WACV61042.2026.00207},

issn = {2642-9381},

year  = {2026},

date = {2026-03-01},

urldate = {2026-03-01},

booktitle = {2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},

pages = {2083\textendash2093},

abstract = {Large-scale livestock operations pose significant risks to human health and the environment, while also being vulnerable to threats such as infectious diseases and extreme weather events. As the number of such operations continues to grow, accurate and scalable mapping has become increasingly important. In this work, we present an infrastructure-first, explainable pipeline for identifying and characterizing Concentrated Animal Feeding Operations (CAFOs) from aerial and satellite imagery. Our method (i) detects candidate infrastructure (e.g., barns, feedlots, manure lagoons, silos) with a domain-tuned YOLOv8 detector, then derives SAM2 masks from these boxes and filters component-specific criteria; (ii) extracts structured descriptors (e.g., counts, areas, orientations, and spatial relations) and fuses them with deep visual features using a lightweight spatial cross-attention classifier; and (iii) outputs both CAFO type predictions and mask-level attributions that link decisions to visible infrastructure. Through comprehensive evaluation, we show that our approach achieves state-of-the-art performance, with Swin-B+PRISM-CAFO surpassing the best performing baseline by up to 15%. Beyond strong predictive performance across diverse U.S. regions, we run systematic gradient\textendashactivation analyses that quantify the impact of domain priors and show how specific infrastructure (e.g., barns, lagoons) shapes classification decisions. We release code, infrastructure masks, and descriptors to support transparent, scalable monitoring of livestock infrastructure, enabling risk modeling, change detection, and targeted regulatory action. Github: https://github.com/Nibir088/PRISM-CAFO.},

note = {ISSN: 2642-9381},

keywords = {Antennas, Circuits and systems, classification, Earth Observing System, Feeds, Filtering, Filters, infrastructure-guided vision, interpretibility, Location awareness, masked attention pooling, multimodal feature fusion, objecj detection, Pixel, Remote Sensing, Satellite images, Segmentation, Sentinel-2, spatial reasoning, synthetic infrastructure mask},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing},

author = {Oishee Bintey Hoque, Nibir Chandra Mandal, Abhijin Adiga, Samarth Swarup, Sayjro Kossi Nouwakpo, Amanda Wilson, Madhav Marathe},

doi = { https://doi.org/10.48550/arXiv.2505.08302},

year  = {2025},

date = {2025-08-22},

urldate = {2025-08-22},

organization = {International Joint Conferences on Artificial Intelligence},

abstract = {Accurate mapping of irrigation methods is crucial for sustainable agricultural practices and food systems. However, existing models that rely solely on spectral features from satellite imagery are ineffective due to the complexity of agricultural landscapes and limited training data, making this a challenging problem. We present Knowledge-Informed Irrigation Mapping (KIIM), a novel Swin-Transformer based approach that uses (i) a specialized projection matrix to encode crop to irrigation probability, (ii) a spatial attention map to identify agricultural lands from non-agricultural

lands, (iii) bi-directional cross-attention to focus complementary information from different modalities, and (iv) a weighted ensemble for combining predictions from images and crop information. Our experimentation on five states in the US shows up to 22.9% (IoU) improvement over baseline with a 71.4% (IoU) improvement for hard-to-classify drip irrigation. In addition, we propose a two-phase transfer learning approach to enhance cross-state irrigation mapping, achieving a 51% IoU boost in a state with limited labeled data. The ability to achieve baseline performance with only 40% of the training data highlights its efficiency, reducing the dependency on extensive manual labeling efforts and making large-scale, automated irrigation mapping more feasible and cost-effective. Code: https://github.com/Nibir088/KIIM},

keywords = {Deep learning, Irrigation, Mapping, Remote Sensing},

pubstate = {published},

tppubtype = {inproceedings}

}