arXiv Computer Vision

The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation

PhiSat‑2 offers 4.75 m multispectral imagery that can be used for monocular building‑height estimation. The authors created the PhiSat‑2–Height Dataset (PHDataset), comprising 9,475 co‑registered patch pairs from 26 cities, and developed the Two‑Stream Ordinal Network (TSONet) that uses multiscale decoupled height representation and footprint‑conditioned height reconstruction. In city‑disjoint tests, TSONet improves building RMSE and MAE by 3.64 % and 4.49 % over the best existing method, and PhiSat‑2 outperforms Sentinel‑2 and Landsat 8, demonstrating its value for urban height mapping.

arXiv Machine Learning
Aug 19

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

The study presents a method for estimating building heights in a large Brazilian city using freely available satellite data, including TerraSAR-X StripMap, PlanetScope, and Sentinel-1. A geographically weighted random forest model achieved an RMSE of 5.34 m and an R² of 0.756 against LiDAR reference data, with local feature importance varying by building type and context. The results highlight that no single sensor dominates across all scenarios, offering guidance for selecting satellite-derived products in different urban settings.

By Guilherme Iablonovski, Pierre-Louis Frison, Tatiana Silva da Silva
Hugging Face Trending Papers
Aug 18

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

The paper presents a method for estimating building heights in a large Brazilian city using freely available satellite data, including TerraSAR‑X StripMap, PlanetScope, and Sentinel‑1. By integrating these sources in a geographically weighted random forest, the authors achieve an RMSE of 5.34 m and an R² of 0.756 against a LiDAR reference. The study also reveals that different predictors dominate in different urban contexts, offering guidance on sensor selection for building‑height mapping.

arXiv Computer Vision
Sep 24

A comparative assessment of global building and settlement datasets across geographic and settlement contexts

arXiv:2609.28154v1 Announce Type: new Abstract: Global building and settlement datasets increasingly support population mapping, exposure assessment, urban monitoring, and other analyses of the built...

By Rufai Omowunmi Balogun, Caroline Margaux Gevaert, Capucine Riom, Derrick Mirindi, Aaron Opdyke, Hamed Alemohammad, Pierre Chrzanowski, Edward Charles Anderson
arXiv Computer Vision
Oct 2

Seeing the City or Recognizing the Place? What Street-View Imagery Adds Beyond Existing Urban Data in VLM Urban Sensing

The study evaluates how much street‑view imagery contributes to urban attribute prediction beyond existing public data. By comparing image‑based models with seven attributes from five public sources and three vision‑language models, the authors find that images outperform other data for building type, function, and low‑rise floor count, while existing data match or exceed image performance for road damage, curb ramps, and house price. The benefit of images varies with visual legibility and local data coverage, suggesting that image value depends on how well the scene is captured and how much complementary data is available.

By Kaizhen Tan
Hugging Face Trending Papers
Jul 22

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.

arXiv Machine Learning
Jun 19

Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision

arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.

By Luke J. Zachmann, David D. Diaz, Vincent A. Landau, Chelsey Walden-Schreiner, Tony Chang, Nathan E. Rutenbeck, Katharyn A. Duffy, Kiarie Ndegwa, Andreas Gros, Scott Conway, Guy Bayes