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: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...
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.
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.
arXiv:2511. 19277v2 Announce Type: replace Abstract: Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning.
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.
arXiv:2606. 28390v1 Announce Type: cross Abstract: Geospatial vector data quality is a foundational research topic in GIS, yet classic rule-based quality assessment algorithms often struggle with diverse urban morphologies and massive data volumes.
DeepC4 is a deep learning-based spatial disaggregation method that uses local census statistics as cluster-level constraints and incorporates multiple conditional label relationships in a multitask learning framework. Applied to Rwandan urban morphology, it achieves macro‑F1 scores of 0.63, 0.78, and 0.45 for roof, wall, and height prediction, respectively, and estimates national dwelling and occupant counts within about 1.1% error compared to census records. The approach outperforms existing GEM and METEOR methods and covers 32‑49% more 500‑meter grid pixels across provinces.
AlphaEarth, a satellite foundation model, maps Earth’s surface into numerical embeddings that allow comparison across places and time. An audit of its representations for 1,000 urban areas in 162 countries shows that cities occupy a distinct but overlapping region on the hypersphere, with continent, climate, and degrees of urbanisation explaining a portion of the variation. The study finds that cities in developing countries exhibit less contrast in vegetation and texture, and that annual changes in a city’s representation are largely driven by model updates rather than pixel changes.
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.
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.
arXiv:2603.00604v2 Announce Type: replace Abstract: High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to o...
arXiv:2607. 13558v1 Announce Type: new Abstract: Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring.