ArchiveLens is a generative spatial knowledge framework that transforms historical maps into entity‑centric, machine‑queryable geospatial databases. The pipeline separates morphological and semantic reconstruction into three stages: building footprint extraction with tiled YOLOv11‑seg, deterministic material classification using HSV color thresholds, and localized attribute parsing via a quantized vision‑language model with domain‑specific prompts. Applied to 445 Sanborn map sheets of Chicago (1918‑1950), the system delivers high geometric and semantic accuracy and enables researchers to query building attributes and compare spatial patterns across time.
arXiv:2607. 17999v1 Announce Type: new Abstract: Spatial understanding is crucial for foundation models (FMs), and maps have long helped humans organize and reason about geographic information.
The paper introduces Semap, an open benchmark dataset of 1,439 manually annotated map patches that captures the diversity of historical map collections. It proposes a generalizable semantic segmentation workflow that combines procedural data synthesis with multiscale integration, achieving the best performance among evaluated methods on heterogeneous map data. The study demonstrates that segmentation accuracy remains stable across different map styles, scales, geographic regions, and publication contexts, thereby facilitating the integration of diverse cartographic archives into historical geographic research.
Spatial understanding is crucial for foundation models (FMs), and maps have long helped humans organize and reason about geographic information. This study examines whether choropleth maps remain useful for machine spatial understanding when models can directly process structured geodata.
arXiv:2602.02154v2 Announce Type: replace Abstract: Prior to modern Earth observation technologies, historical maps provide a unique record of long-term urban transformation and offer a lens on the e...
arXiv:2610.16320v2 Announce Type: replace Abstract: Building age is fundamental for urban infrastructure safety, operation, and planning, as it directly affects structural safety, and sustainable dev...
arXiv:2606. 24997v1 Announce Type: new Abstract: Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural network.
CitySTAR introduces a training‑free framework that transforms billion‑scale urban point clouds into a query‑ready scene graph of open‑vocabulary 3D instances, using CodeLLM‑driven tools to supply multimodal evidence for node attributes and spatial relations. It models target‑context topology with paired hypergraphs and performs bidirectional topology verification for structural disambiguation, followed by a Reflective Cross‑modal Grounding module that integrates topology consistency and 2D visual evidence to decide over a metric‑aware 3D context graph. The authors also present CitySTAR‑3D, a benchmark that enhances semantic coverage, instance completeness, bounding‑box fidelity, and spatial‑relation complexity for city‑scale 3D grounding, and report extensive experiments showing consistent improvements in open‑world urban 3D grounding with strong interpretability and generalization.
arXiv:2608. 20026v1 Announce Type: cross Abstract: Streetscape quality has become a central concern in contemporary urban planning, particularly within the framework of the pedestrian-friendly 15-minute city, where walkability and public-space quality are increasingly recognized as key determinants of urban performance.
arXiv:2505. 02271v2 Announce Type: replace Abstract: The proliferation of Generative Artificial Ingelligence (AI), especially Large Language Models, presents transformative opportunities for urban applications through Urban Foundation Models.
PlaceSeek is a human‑centered geospatial retrieval framework that maps natural‑language queries to street‑view images by decomposing queries into functional and affective sub‑intents. It uses a Semantic Grounding Module to verify that candidate images contain the physical evidence needed for the intended activity, and an Affective Alignment Module to re‑rank these candidates based on human urban perception judgments. Evaluated on 31,956 Milan street‑view locations, PlaceSeek achieves high precision and ranking metrics, outperforming several vision‑language baselines and demonstrating the importance of both physical grounding and affective alignment for complex urban spatial queries.
arXiv:2611.05697v1 Announce Type: new Abstract: Finding specific objects, structures, and spatial patterns supports critical work in agriculture, infrastructure planning, environmental monitoring, an...
arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.