OpenStreetMap (OSM) has transitioned from a pioneering volunteered geographic information project into a global, multi-disciplinary research nexus. This study presents a bibliometric and systematic analysis of the OSM research landscape, examining its development trajectory and key driving forces. By evaluating 1926 publications from the Web of Science (WoS) Core Collection and 782 State of the Map (SotM) presentations up to June 2024, we quantify publication growth, collaboration patterns, and thematic evolution. Results demonstrate simultaneous consolidation and diversification within the field. While a stable core of contributors continues to anchor OSM research, themes have shifted from initial concerns over data production and quality toward advanced analytical and applied uses. Comparative analysis of OSM-related research in WoS and SotM reveals distinct but complementary agendas between scholars and the OSM community. Building on these findings, we identify six emerging research directions and discuss how evolving partnerships among academia, the OSM community, and industry are poised to shape the future of OSM research. This study establishes a structured reference for understanding the state of OSM studies and offers strategic pathways for navigating its future trajectory.

This work introduces a dataset for automated earthquake-damaged building detection from post-event satellite imagery. Using very high-resolution Synthetic Aperture Radar (SAR) and optical data from the 2023 Turkey-Syria earthquakes, the dataset includes over four thousand co-registered building footprints and patches. The task is framed as a binary image classification problem, serving as a reference for researchers to expedite algorithm development for rapid damaged building detection in future events. The dataset and codes together with detailed explanations will be made publicly available at https://github.com/ya0-sun/PostEQ-SARopt-BuildingDamage.

Quick and automated earthquake-damaged building detection from post-event satellite imagery is crucial, yet it is challenging due to the scarcity of training data required for developing robust algorithms. This letter presents the first dataset dedicated to detecting earthquake-damaged buildings from post-event very high resolution (VHR) Synthetic Aperture Radar (SAR) and optical imagery. Utilizing open satellite imagery and annotations acquired after the 2023 Turkey–Syria earthquakes, we deliver a dataset of co-registered building footprints and satellite image patches of both SAR and optical data, encompassing more than four thousand buildings. The task of damaged building detection is formulated as a binary image classification problem, that can also be treated as an anomaly detection problem due to extreme class imbalance. We provide baseline methods and results to serve as references for comparison. Researchers can utilize this dataset to expedite algorithm development, facilitating the rapid detection of damaged buildings in response to future events. The dataset and codes together with detailed explanations and visualization will be made publicly available at https://github.com/ya0-sun/PostEQ-SARopt-BuildingDamage.

In the remote sensing community, extracting buildings from remote sensing imagery has triggered great interest. While many studies have been conducted, a comprehensive review of these approaches that are applied to optical and synthetic aperture radar (SAR) imagery is still lacking. Therefore, we provide an in-depth review of both early efforts and recent advances, which are aimed at extracting geometrical structures or semantic attributes of buildings, including building footprint generation, building facade segmentation, roof segment and superstructure segmentation, building height retrieval, building type classification, building change detection, and annotation data correction. Furthermore, a list of corresponding benchmark datasets is given. Finally, challenges and outlooks of existing approaches as well as promising applications are discussed to enhance comprehension within this realm of research.

Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8 satellite’s surface temperature product. A proof-of-concept numerical experiment utilizes corresponding remote sensing data for the city of New York to verify the cooling effect of urban forests.

In this paper, we propose a bounding box regression network for building height retrieval using a single TerraSAR - X stripmap image. The proposed network employs building footprints from GIS data and exploits the location relationship between a building's footprint and its bounding box, enabling fast computation. Experimental results over Rotterdam show that the proposed network can reduce the computation cost significantly while keeping the height accuracy of individual buildings compared to a Faster R-CNN based method.

Object retrieval and reconstruction from very-high-resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging due to the complexity of SAR data. This article addresses the issue of individual building segmentation from a single VHR SAR image in large-scale urban areas. To achieve this, we introduce building footprints from geographic information system (GIS) data as a complementary information and propose a novel conditional GIS-aware network (CG-Net). The proposed model learns multilevel visual features and employs building footprints to normalize the features for predicting building masks in the SAR image. We validate our method using a high-resolution spotlight TerraSAR-X image collected over Berlin. Experimental results show that the proposed CG-Net effectively brings improvements with variant backbones. We further compare two representations of building footprints, namely, complete building footprints and sensor-visible footprint segments, for our task, and conclude that the use of the former leads to better segmentation results. Moreover, we investigate the impact of inaccurate GIS data on our CG-Net, and this study shows that CG-Net is robust against positioning errors in the GIS data. In addition, we propose an approach of ground truth generation of buildings from an accurate digital elevation model (DEM), which can be used to generate large-scale SAR image data sets. The segmentation results can be applied to reconstruct 3-D building models at level-of-detail (LoD) 1, which is demonstrated in our experiments.

3D building reconstruction from monocular remote sensing imagery is a promising and economical way to generate 3D city models at a large scale, yet the task is rarely touched. The paper tackles the problem via an end-to-end network. The goal is achieved by a modified network, named Mask-Height R-CNN, based on Mask R-CNN, with an additional height prediction head in the Region Proposal Network (RPN). Unlike most deep learning based methods, the height estimation is done on the instance level instead of pixel level, which does not require the assembly of the height maps and building masks. The proposed network gains good performances on ISPRS datasets, with 3D F1 scores of over 0.8.

In this paper, we propose a network for individual building segmentation from a single VHR SAR image. The proposed network employs building footprints from GIS data in learning multi-level visual features to predict building masks in the SAR image. Experimental results over Berlin show that the proposed network effectively brings improvements with variant backbones. In addition, we propose an approach for generating building labels from an accurate digital elevation model (DEM), which can be used to generate large-scale SAR image datasets.

Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods often lead to segmentation masks with blurred boundaries. In this paper, we propose a novel instance segmentation network for building segmentation in high-resolution remote sensing images. More specifically, we consider segmenting an individual building as detecting several keypoints. The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building. By doing so, the sharp boundary of the building could be preserved. Experiments are conducted on selected Aerial Imagery for Roof Segmentation (AIRS) dataset, and our method achieves better performance in both quantitative and qualitative results with comparison to the state-of-the-art methods. Our network is a bottom-up instance segmentation method that could well preserve geometric details.

In this paper, we propose a framework for the automatic registration of GIS building footprint polygons to a corresponding SAR image through the corresponding features of building walls in the two data. To extract feature lines, the Potts model is adopted for SAR image segmentation, and visibility test is performed on both data. The feature lines are then sampled to two point sets, and are registered using Iterative Closest Point (ICP) algorithm. The test result shows a registration accuracy of 0.67 m in azimuth direction, and 1.64m in range direction.