AI & Environment Resource HubSmith et al. · 2025-04-23
Dargana is a fine-tuned EarthPT model that maps dynamic tree canopy cover from space with high accuracy using minimal data and compute, enabling detailed, time-sensitive land monitoring for conservation.
Smith et al.. 2025-04-23. Dargana: Fine-Tuning EarthPT for Dynamic Tree Canopy Mapping From Space. https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/iclr2025/11/paper.pdf (AI & Environment Resource Hub; record paper-255; collection snapshot 2026-09-15).
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Source sheet: Scientific Paper · Row 259 · Original ID: paper-255.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Research tools and geospatial methods; Land use, heat, and noise; Biodiversity and ecosystems
This study developed a high-resolution global map using satellite data and a neural network to classify the dominant drivers of forest loss from 2001 to 2022, revealing that permanent agriculture is the leading cause worldwide, and aims to support more effective policy, conservation, and supply chain monitoring at multiple scales.
Shared topics: Research tools and geospatial methods; Biodiversity and ecosystems
Trust is a cornerstone and enabler of human civilization, determining the very nature of how people interact with each other. The swift integration of artificial intelligence (AI) into daily life poses grand societal challenges and necessitates a reevaluation of trust. Our bibliometric literature review calls for scientists and stakeholders to cross traditional academic boundaries to address emerging and evolving societal challenges arising from AI. We propose a transdisciplinary research framework to understand and bolster trust in AI and address grand challenges in domains as diverse and urgent as misinformation, discrimination, and warfare.
Shared topics: Research tools and geospatial methods; Biodiversity and ecosystems
This study introduces a new foundation model based on the Prithvi-EO Vision Transformer, pre-trained on Sentinel-3 ocean color data, to enhance marine Earth observation. By fine-tuning on chlorophyll and primary production tasks, the model outperforms traditional baselines, showing that self-trained AI can extract detailed spatial patterns from limited labeled data and improve monitoring of ocean ecosystems and climate processes.
Shared topics: Research tools and geospatial methods; Biodiversity and ecosystems
Adaptix Earth provides cutting-edge geospatial analytics and biodiversity risk assessment to help organizations make informed environmental decisions.