Research and application of deep learning-based framework for monitoring and detecting new illegal construction

doi:10.13474/j.cnki.11-2246.2023.0338

Abstract

Abstract: Scientific and intelligent control of new illegal constructions is an inevitable requirement for high-quality urban development. Aiming at the characteristics of new illegal constructions with diverse types, strong concealment, administrative intersection and difficult disposal, this paper adopts a building boundary extraction algorithm based on high-resolution remote sensing images and management data of functional departments, coupled with FPN and Mask-RCNN multi-task fusion. It constructs a whole chain management framework of new illegal constructions from “Construction Behavior Monitoring-Multi-source Data Fusion Analysis-Collaborative Assignment Governance”, which provides technical support for dynamic monitoring and precise management under the high-altitude perspective of the city.

Key words: new illegal construction, deep learning, high resolution, the whole chain of management framework

CLC Number:

P237

KANG Tingjun, CHEN Ruixin, SUN Ying, XIA Yixiong, WANG Bin. Research and application of deep learning-based framework for monitoring and detecting new illegal construction[J]. Bulletin of Surveying and Mapping, 2023, 0(11): 116-121.

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