Plan2Map: A Multimodal Benchmark for Document-Grounded Geospatial Boundary Reconstruction from Planning Records
Plan2Map is a 208-case multimodal benchmark for reconstructing geospatial boundaries from UK planning records. The proposed GeoPlanAgent system achieves 0.736 mean IoU and 0.904 median IoU, substantially outperforming direct VLM baselines, with errors concentrated in localization and map registration.
[2606.02747] Plan2Map: A Multimodal Benchmark for Document-Grounded Geospatial Boundary Reconstruction from Planning Records
[Submitted on 1 Jun 2026]
Title:Plan2Map: A Multimodal Benchmark for Document-Grounded Geospatial Boundary Reconstruction from Planning Records
View a PDF of the paper titled Plan2Map: A Multimodal Benchmark for Document-Grounded Geospatial Boundary Reconstruction from Planning Records, by Fabian Degen and 6 other authors
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Abstract:Planning records define restrictions over geographic areas, but their source documents often provide only indirect spatial evidence rather than machine-readable boundaries. We introduce Plan2Map, a 208-case multimodal benchmark for document-grounded geospatial boundary reconstruction from UK planning records. Given only a source planning document, systems must reconstruct a valid geospatial boundary from notice text, schedules, map plates, map labels, and boundary annotations; the reference GeoJSON is held out for scoring. We propose GeoPlanAgent, a document-grounded, geospatial-tool-in-the-loop system that decomposes the task into evidence extraction, localisation, map registration, boundary segmentation, projection, and verification. On Plan2Map, GeoPlanAgent achieves 0.736 mean IoU and 0.904 median IoU, with 67.8\% of predictions at or above 0.8 IoU, substantially outperforming direct VLM-to-GeoJSON baselines. Diagnostic analysis shows that direct VLM prediction remains unreliable, while remaining errors are concentrated in localisation and map registration, and supervised boundary segmentation substantially improves pixel-level mask quality. Plan2Map provides a concrete testbed for multimodal geospatial reconstruction from public planning records. Project page: this https URL.
Comments: Project page: this https URL. Fabian Degen and Oishi Deb Contributed Equally
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.02747 [cs.CV]
(or arXiv:2606.02747v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.02747
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Oishi Deb [view email] [v1] Mon, 1 Jun 2026 18:12:16 UTC (3,531 KB)
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