Research Publications

Our machine learning and research team has presented at leading AI, Climate and Geospatial conferences:

  • Soil Organic Carbon Estimation from Climate-related Features with Graph Neural Network | 2023 | NeurIPS (paper)

  • AI for Agriculture: The Comparison of Semantic Segmentation Methods for Crop Mapping with Sentinel-2 Imagery | 2023 | (paper)

  • Autonomous monitoring of environmental condition and overgrazing in East-African rangelands through remote sensing

    | 2021 | NeurIPS (paper)

  • Biophysical Parameter Estimation Using Earth Observation Data in a Multi-Sensor Data Fusion Approach: CycleGAN | 2021 | IEEE International Geoscience & Remote Sensing Symposium (paper)

  • Optimal use of Multi-Spectral Satellite Data with Convolutional Neural Networks | 2020 | Harvard CRCS AI for Social Good Workshop (paper)

  • SMArtCast: Predicting Soil Moisture Interpolations into the future using Earth Observation data in a Deep Learning Framework | 2020 | ICLR Climate Change AI (paper)

  • AI-Based Evaluation of the SDGs: The case of crop detection with Earth Observation Data | 2019 | ICLR AI For Good WorkShop (paper)

  • Prediction of Soil Moisture Content Based On Satellite Data and Sequence-to-Sequence Networks | 2018 | NeurIPS (paper)