The paper revisits building damage assessment (BDA) from satellite imagery, emphasizing the need for a sensor‑agnostic, instance‑based task definition that can handle missing pre‑event data. It reviews existing datasets by modality, timing, and geography, critiques current single‑score evaluation practices, and proposes a more robust, ranking‑based multi‑score protocol. The authors also survey deep learning methods, highlight challenges in generalizing to new events and regions, and outline how these insights could shape future BDA challenges and data‑sharing practices.
By Anthony Cioppa, Virgile Blua, S\'ebastien Pi\'erard, Pietro Ceccato, Marc Van Droogenbroeck
arXiv:2606. 14963v1 Announce Type: cross Abstract: Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery.
By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni
arXiv:2509. 09794v5 Announce Type: replace Abstract: Computational models have emerged as powerful tools for multi-scale energy modeling research at the building and urban scale, supporting data-driven analysis across building and urban energy systems.
By Jackson Eshbaugh, Chetan Tiwari, Jorge Silveyra
arXiv:2607. 07292v1 Announce Type: cross Abstract: Accurately estimating urban carbon emissions is critical for sustainable urban planning, yet many existing approaches remain difficult to apply consistently across cities due to data-source heterogeneity and the lack of fine-grained semantic-temporal context in remote sensing data.
By Zeru Yang, Fang-Ying Gong, Steve H. L. Yim, Chau Yuen
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.
By Joshua Dimasaka, Christian Gei{\ss}, Emily So
arXiv:2606. 15890v1 Announce Type: new Abstract: Understanding urban wellbeing from multimodal data requires integrating heterogeneous spatial and temporal signals, posing significant challenges for current multimodal large language models (MLLMs).
By Yanxin Xi, Xiang Su, Jie Feng, Yu Liu, Sasu Tarkoma, Pan Hui
The study investigates shortcut learning in geospatial foundation model embeddings for cross-disaster damage assessment. By holding out entire disasters during evaluation, the authors demonstrate a significant drop in mean balanced accuracy across multiple datasets, revealing that models rely on event or scene characteristics rather than transferable evidence. They further show that simple feature-level transformations, such as supervised contrastive learning or PCA, can improve generalization to unseen disasters.
By Ali Shibli, Andrea Nascetti, Yifang Ban
arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.
By Shikha V. Chandel, Yadav Raj Ghimire, Timothy Agboada, Leila Hashemi-Beni
arXiv:2609.37031v1 Announce Type: new
Abstract: Building extraction from optical remote sensing (RS) imagery is fundamental to urban mapping, yet existing methods are often dataset-specific and gener...
By Wei Huang, Chenying Liu, Yilei Shi, Xiao Xiang Zhu
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.
By Yanjiao Song, Bowen Cai, Timo Balz, Zhenfeng Shao, Neema Simon Sumari, James Magidi, Walter Musakwa
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 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