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Oishee Bintey Hoque

4 accepted papers

2026

AG-STELLA: Spatio-Temporal Learning for Water-related Agricultural Land Use Activity Mapping with AlphaEarth

Nibir Chandra Mandal, Oishee Bintey Hoque, Kyle Luong, Samarth Swarup, Kirti Rajagopalan, Mandy Wilson +2
IJCAI 2026

Accurate mapping of agricultural land use activity, particularly long-term transition from cropland to pasture and short-term transition between cropland to fallow land, is essential for sustainable water management, drought response, and food-system resilience which directly supports United Nations

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2025

IGraSS: Learning to Identify Infrastructure Networks from Satellite Imagery by Iterative Graph-constrained Semantic Segmentation

Oishee Bintey Hoque, Abhijin Adiga, Aniruddha Adiga, Siddharth Chaudhary, Madhav V. Marathe, S.S. Ravi +3
IJCAI 2025

Accurate canal network mapping is essential for water management, including irrigation planning and infrastructure maintenance. State-of-the-art semantic segmentation models for infrastructure mapping, such as roads, rely on large, well-annotated remote sensing datasets. However, incomplete or inade

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2025

IRRISIGHT: A Large-Scale Multimodal Dataset and Scalable Pipeline to Address Irrigation and Water Management in Agriculture

Nibir Chandra Mandal, Oishee Bintey Hoque, Mandy L Wilson, Samarth Swarup, Sayjro K Nouwakpo, Abhijin Adiga +1
NeurIPS 2025poster

The lack of fine-grained, large-scale datasets on water availability presents a critical barrier to applying machine learning (ML) for agricultural water management. Since there are multiple natural and anthropogenic factors that influence water availability, incorporating diverse multimodal feature…

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2025

Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing

Oishee Bintey Hoque, Nibir Chandra Mandal, Abhijin Adiga, Samarth Swarup, Sayjro Kossi Nouwakpo, Amanda Wilson +1
IJCAI 2025

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 c

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