A locally-constrained YOLO framework for detecting small and densely-distributed building footprints

Building footprints are among the most predominant features in urban areas, and provide valuable information for urban planning, solar energy suitability analysis, etc. We aim to automatically and rapidly identify building footprints by leveraging deep learning techniques and the increased availabil...

Full description

Saved in:
Bibliographic Details
Published in:International journal of geographical information science : IJGIS Vol. 34; no. 4; pp. 777 - 801
Main Authors: Xie, Yiqun, Cai, Jiannan, Bhojwani, Rahul, Shekhar, Shashi, Knight, Joseph
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 02.04.2020
Taylor & Francis LLC
Subjects:
Building detection
data collection
Datasets
Deep learning
Footprints
information science
locally constrained
Machine learning
Object recognition
prediction
Regression analysis
Remote sensing
Robustness (mathematics)
Solar energy
spatial data
Spatial resolution
Urban areas
Urban planning
YOLO
ISSN:1365-8816, 1362-3087, 1365-8824
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Building footprints are among the most predominant features in urban areas, and provide valuable information for urban planning, solar energy suitability analysis, etc. We aim to automatically and rapidly identify building footprints by leveraging deep learning techniques and the increased availability of remote sensing datasets at high spatial resolution. The task is computationally challenging due to the use of large training datasets and large number of parameters. In related work, You-Only-Look-Once (YOLO) is a state-of-the-art deep learning framework for object detection. However, YOLO is limited in its capacity to identify small objects that appear in groups, which is the case for building footprints. We propose a LOcally-COnstrained (LOCO) You-Only-Look-Once framework to detect small and densely-distributed building footprints. LOCO is a variant of YOLO. Its layer architecture is determined by the spatial characteristics of building footprints and it uses a constrained regression modeling to improve the robustness of building size predictions. We also present an invariant augmentation based voting scheme to further improve the precision in the prediction phase. Experiments show that LOCO can greatly improve the solution quality of building detection compared to related work.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1365-8816
1362-3087
1365-8824
DOI:10.1080/13658816.2019.1624761