{"total":19,"rows":[{"id":"2312.10114","title":"FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring","created":"2023-12-15","cited_by_count":31,"area":null},{"id":"2311.12056","title":"Kuro Siwo: 33 billion $m^2$ under the water. A global multi-temporal satellite dataset for rapid flood mapping","created":"2023-11-18","cited_by_count":16,"area":null},{"id":"2404.03936","title":"Deep Learning for Satellite Image Time Series Analysis: A Review","created":"2024-04-05","cited_by_count":13,"area":null},{"id":"2401.11519","title":"CaBuAr: California Burned Areas dataset for delineation","created":"2024-01-21","cited_by_count":9,"area":null},{"id":"2312.00069","title":"SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters","created":"2023-11-29","cited_by_count":8,"area":null},{"id":"2406.04230","title":"M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data","created":"2024-06-06","cited_by_count":6,"area":null},{"id":"2210.01272","title":"A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture","created":"2022-10-03","cited_by_count":4,"area":null},{"id":"2205.07721","title":"Towards Space-to-Ground Data Availability for Agriculture Monitoring","created":"2022-05-16","cited_by_count":3,"area":null},{"id":"2608.09497","title":"SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping","created":"2026-08-10","cited_by_count":3,"area":null},{"id":"2506.11740","title":"AgriPotential: A Novel Multi-Spectral and Multi-Temporal Remote Sensing Dataset for Agricultural Potentials","created":"2025-06-13","cited_by_count":2,"area":null},{"id":"2505.19159","title":"A Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic Segmentation","created":"2025-05-25","cited_by_count":1,"area":null},{"id":"2510.16207","title":"Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutions","created":"2025-10-17","cited_by_count":1,"area":null},{"id":"2410.09032","title":"Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery","created":"2024-10-11","cited_by_count":0,"area":null},{"id":"2411.00967","title":"Phenology-based learning framework for yield estimation and harvest forecasting of raspberry fruits","created":"2024-11-01","cited_by_count":0,"area":null},{"id":"2411.18475","title":"Weakly Supervised Framework Considering Multi-temporal Information for Large-scale Cropland Mapping with Satellite Imagery","created":"2024-11-27","cited_by_count":0,"area":null},{"id":"2506.12885","title":"$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series","created":"2025-06-15","cited_by_count":0,"area":null},{"id":"2606.00676","title":"A Modelling and Evaluation Framework for EuroCrops-Driven Sentinel-2 Crop Segmentation","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2609.26809","title":"AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2610.08810","title":"Transferability and operational reliability of a Prithvi crop classification foundation model under phenological and geographic shift across three continents","created":"2026-09-20","cited_by_count":0,"area":null}],"next_cursor":null}