AI & Environment Resource HubSzwarcman, Daniela; Roy, Sujit; Fraccaro, Paolo; Gíslason, Þorsteinn Elí; Blumenstiel, Benedikt; Ghosal, Rinki; de Oliveira, Pedro Henrique; Almeida, Joao Lucas de Sousa; Sedona, Rocco; Kang, Yanghui; Chakraborty, Srija; Wang, Sizhe; Gomes, Carlos; Kumar, Ankur; Truong, Myscon; Godwin, Denys; Lee, Hyunho; Hsu, Chia-Yu; Lal, Rohit; Asanjan, Ata Akbari; Mujeci, Besart; Shidham, Disha; Keenan, Trevor; Arevalo, Paulo; Li, Wenwen; Alemohammad, Hamed; Olofsson, Pontus; Hain, Christopher; Kennedy, Robert; Zadrozny, Bianca; Bell, David; Cavallaro, Gabriele; Watson, Campbell; Maskey, Manil; Ramachandran, Rahul; Moreno, Juan Bernabe · 2025-02-03
Prithvi-EO-2.0, a geospatial foundation model trained on NASA satellite data, outperforms its predecessor and other models in remote sensing tasks, aiding applications like disaster response and ecosystem monitoring.
Szwarcman, Daniela; Roy, Sujit; Fraccaro, Paolo; Gíslason, Þorsteinn Elí; Blumenstiel, Benedikt; Ghosal, Rinki; de Oliveira, Pedro Henrique; Almeida, Joao Lucas de Sousa; Sedona, Rocco; Kang, Yanghui; Chakraborty, Srija; Wang, Sizhe; Gomes, Carlos; Kumar, Ankur; Truong, Myscon; Godwin, Denys; Lee, Hyunho; Hsu, Chia-Yu; Lal, Rohit; Asanjan, Ata Akbari; Mujeci, Besart; Shidham, Disha; Keenan, Trevor; Arevalo, Paulo; Li, Wenwen; Alemohammad, Hamed; Olofsson, Pontus; Hain, Christopher; Kennedy, Robert; Zadrozny, Bianca; Bell, David; Cavallaro, Gabriele; Watson, Campbell; Maskey, Manil; Ramachandran, Rahul; Moreno, Juan Bernabe. 2025-02-03. Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications. https://arxiv.org/abs/2412.02732 (AI & Environment Resource Hub; record paper-033; collection snapshot 2026-09-15).
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Shared topics: Research tools and geospatial methods; Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation
This study presents a new machine learning (Light Gradient Boosting Machine) approach to generate high-resolution, hourly maps of gross primary productivity (GPP) for East Asia (2020–2021). The paper highlights clear diurnal patterns across different land cover types and latitudes, offering valuable insights for ecosystem monitoring and carbon cycle modeling.
Shared topics: Research tools and geospatial methods; Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation
This paper introduces TESSERA, an open global remote sensing foundation model that fuses optical and radar satellite time series using Transformer-based encoders to produce ready-to-use 10 m embeddings, outperforming or matching state-of-the-art models across diverse ecological and agricultural tasks.
Shared topics: Research tools and geospatial methods; Climate and greenhouse gases; Weather, hazards, and adaptation
Self-supervised learning enhances 3D cloud reconstruction, improving climate predictions by reducing uncertainties in climate models.
Shared topics: Research tools and geospatial methods; Climate and greenhouse gases; Weather, hazards, and adaptation
This study showcases a machine learning hackathon in France where a CNN-based model accurately predicted flood risk evolution using geospatial and climate data, even without streamflow input, emphasizing trustworthy AI in data-scarce regions.