Geospatial AI and Emerging Opportunities for Climate Action
Cathy Richards and Izni Zahidi on the democratisation of map-making, and the ethical risks arising from the use of GIS data.
AI & Environment Resource HubLück, Sarah; Callaghan, Max; Borchers, Malgorzata; Cowie, Annette; Fuss, Sabine; Gidden, Matthew; Hartmann, Jens; Kammann, Claudia; Keller, David P.; Kraxner, Florian; Lamb, William F.; Mac Dowell, Niall; Müller-Hansen, Finn; Nemet, Gregory F.; Probst, Benedict S.; Renforth, Phil; Repke, Tim; Rickels, Wilfried; Schulte, Ingrid; Smith, Pete; Smith, Stephen M.; Thrän, Daniela; Troxler, Tiffany G.; Sick, Volker; van der Spek, Mijndert; Minx, Jan C. · 2025-07-18
This study uses machine learning to conduct a large-scale systematic mapping of carbon dioxide removal (CDR) research, identifying nearly 29,000 relevant studies 3–4 times more than previously estimated. It reveals that CDR research is highly concentrated in specific options like biochar, dominated by technology-focused experimental studies, and disproportionately located in China and OECD countries. The study provides an open-access database to support climate assessments and policy decisions, including future IPCC reports.
Lück, Sarah; Callaghan, Max; Borchers, Malgorzata; Cowie, Annette; Fuss, Sabine; Gidden, Matthew; Hartmann, Jens; Kammann, Claudia; Keller, David P.; Kraxner, Florian; Lamb, William F.; Mac Dowell, Niall; Müller-Hansen, Finn; Nemet, Gregory F.; Probst, Benedict S.; Renforth, Phil; Repke, Tim; Rickels, Wilfried; Schulte, Ingrid; Smith, Pete; Smith, Stephen M.; Thrän, Daniela; Troxler, Tiffany G.; Sick, Volker; van der Spek, Mijndert; Minx, Jan C.. 2025-07-18. Scientific Literature on Carbon Dioxide Removal Revealed as Much Larger Through AI-Enhanced Systematic Mapping. https://www.nature.com/articles/s41467-025-61485-8 (AI & Environment Resource Hub; record paper-350; 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 354 · Original ID: paper-350.
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: Governance and society; Research tools and geospatial methods; Climate and greenhouse gases; Weather, hazards, and adaptation
Cathy Richards and Izni Zahidi on the democratisation of map-making, and the ethical risks arising from the use of GIS data.
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.
Shared topics: Research tools and geospatial methods; Climate and greenhouse gases; 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.