Next Article in Journal
Drainage Network Analysis and Structuring of Topologically Noisy Vector Stream Data
Previous Article in Journal
Flood Management in Aqala through an Agent-Based Solution and Crowdsourcing Services in an Enterprise Geospatial Information System
Open AccessArticle

Automatic Identification of Overpass Structures: A Method of Deep Learning

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
National Engineering Research Center for Geographic Information System, Wuhan 430074, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 421;
Received: 22 July 2019 / Revised: 11 September 2019 / Accepted: 16 September 2019 / Published: 18 September 2019
The identification of overpass structures in road networks has great significance for multi-scale modeling of roads, congestion analysis, and vehicle navigation. The traditional vector-based methods identify overpasses by the methodologies coming from computational geometry and graph theory, and they overly rely on the artificially designed features and have poor adaptability to complex scenes. This paper presents a novel method of identifying overpasses based on a target detection model (Faster-RCNN). This method utilizes raster representation of vector data and convolutional neural networks (CNNs) to learn task adaptive features from raster data, then identifies the location of an overpass by a Region Proposal network (RPN). The contribution of this paper is: (1) An overpass labelling geodatabase (OLGDB) for the OpenStreetMap (OSM) road network data of six typical cities in China is established; (2) Three different CNNs (ZF-net, VGG-16, Inception-ResNet V2) are integrated into Faster-RCNN and evaluated by accuracy performance; (3) The optimal combination of learning rate and batchsize is determined by fine-tuning; and (4) Five geometric metrics (perimeter, area, squareness, circularity, and W/L) are synthetized into image bands to enhance the training data, and their contribution to the overpass identification task is determined. The experimental results have shown that the proposed method has good accuracy performance (around 90%), and could be improved with the expansion of OLGDB and switching to more sophisticated target detection models. The deep learning target detection model has great application potential in large-scale road network pattern recognition, it can task-adaptively learn road structure features and easily extend to other road network patterns. View Full-Text
Keywords: road network pattern; overpass; deep learning; target detection model; Faster-RCNN road network pattern; overpass; deep learning; target detection model; Faster-RCNN
Show Figures

Figure 1

MDPI and ACS Style

Li, H.; Hu, M.; Huang, Y. Automatic Identification of Overpass Structures: A Method of Deep Learning. ISPRS Int. J. Geo-Inf. 2019, 8, 421.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop