Hugging Face Trending Papers

Global Building Area Estimation Products: How Accurate Are They?

Geo-spatial rasters of building footprint area are useful for a variety of tasks, such as monitoring urbanization, improving energy efficiency, and tracking greenhouse gas emissions. There are now multiple global building raster datasets, however there lacks an independent, comprehensive, and fair assessment of their accuracy.

Hugging Face Trending Papers
1d ago

Spatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors

Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments yet remains difficult to obtain at city scale in the Global South where airborne LiDAR coverage is rare and commercial very high-resolution imagery is cost-prohibitive or unavailable. While recent works have demonstrated building height estimation using freely available Sentinel imagery, the resolution ceiling of resulting products is still coarse for material stock analysis.

arXiv Machine Learning
22h ago

Spatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors

arXiv:2608. 17822v1 Announce Type: cross Abstract: Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments yet remains difficult to obtain at city scale in the Global South where airborne LiDAR coverage is rare and commercial very high-resolution imagery is cost-prohibitive or unavailable.

By Guilherme Iablonovski, Pierre-Louis Frison, Tatiana Silva da Silva
arXiv Machine Learning
Jul 7

Closing Gaps in Emissions Monitoring with Climate TRACE

arXiv:2511. 19277v2 Announce Type: replace Abstract: Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning.

By Brittany V. Lancellotti, Jordan M. Malof, Aaron Davitt, Gavin McCormick, Shelby Anderson, Pol Carb\'o-Mestre, Gary Collins, Verity Crane, Zoheyr Doctor, George Ebri, Kevin Foster, Trey M. Gowdy, Michael Guzzardi, John Heal, Heather Hunter, David Kroodsma, Khandekar Mahammad Galib, Paul J. Markakis, Gavin McDonald, Daniel P. Moore, Eric D. Nguyen, Sabina Parvu, Michael Pekala, Christine D. Piatko, Amy Piscopo, Mark Powell, Krsna Raniga, Elizabeth P. Reilly, Michael Robinette, Ishan Saraswat, Patrick Sicurello, Isabella S\"oldner-Rembold, Raymond Song, Charlotte Underwood, Kyle Bradbury
arXiv Machine Learning
Jul 31

Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States

arXiv:2607. 27217v1 Announce Type: cross Abstract: Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements.

By Shashika Lamahewage, Chandi Witharana
arXiv Machine Learning
Jun 19

Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland

arXiv:2606. 20034v1 Announce Type: new Abstract: Understanding urban spatial morphology is critical for climate modeling, risk assessment, and sustainable urban design, and Local Climate Zone (LCZ) mapping provides the basic framework for this.

By Htet Yamin Ko Ko, Clement Atzberger
arXiv Machine Learning
Jul 27

Beyond Binary Rooftop Mapping: A Four-Class Deep Learning Framework for Green Roof Potential Assessment from Open Swiss Geospatial Data

arXiv:2607. 22342v1 Announce Type: new Abstract: The development of effective urban climate adaptation strategies requires comprehensive spatial information on rooftops and buildings, since such information underpins the assessment of ecosystem services provided by green infrastructure, particularly for urban heat island (UHI) mitigation.

By Htet Yamin Ko Ko
arXiv AI
Jul 17

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence

arXiv:2607. 14127v1 Announce Type: cross Abstract: Representative clutter height (RCH) is a key parameter in radio propagation and interference analysis because it captures the dominant height of local obstructions that drive terminal clutter loss.

By Shohini Sarkar, Smithi Mahendran, Rishi Chudasama, Varun Mannam, Arav Luthra, Yuvraj Rekhi, Vivek Nadig, Arsh Goenka