{
  "id": 19069,
  "url": "https://arxiv.org/abs/2608.11638v1",
  "title": "Transferable Above-Ground Biomass (AGB) Estimation Model from Multi-Sensor Data with Sparse Field Calibration",
  "summary": "Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable. While field inventories provide high localized accuracy, they are spatially sparse; conversely, spaceborne LiDAR from the Global Ecosystem Dynamics Investigation (GEDI) offers broad biomass samples but lacks spatial continuity and systematic underestimation of high-biomass forests. This paper presents an operational framework centered on a single",
  "authors": "Pann Thinzar Seint, Bryan Atwood, Subas Chhatkuli",
  "category": "research",
  "topics": "environment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T04:36:26.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19069",
  "original_url": "https://arxiv.org/abs/2608.11638v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}