Identifying Streetscape Features Using VHR Imagery and Deep Learning Applications

Deep Learning (DL) based identification and detection of elements in urban spaces through Earth Observation (EO) datasets have been widely researched and discussed. Such studies have developed state-of-the-art methods to map urban features like building footprint or roads in detail. This study delve...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 13; číslo 17; s. 3363
Hlavní autoři: Verma, Deepank, Mumm, Olaf, Carlow, Vanessa Miriam
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2021
Témata:
Algorithms
Braunschweig
Buildings
Computer vision
Corridors
data collection
Datasets
Deep Learning
Identification methods
Image segmentation
Infrastructure
Machine learning
Neural networks
object detection
Object recognition
Remote sensing
road detection
Roads & highways
semantic segmentation
spatial data
State-of-the-art reviews
streetscape
Training
Urban areas
vegetation
ISSN:2072-4292, 2072-4292
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Shrnutí:Deep Learning (DL) based identification and detection of elements in urban spaces through Earth Observation (EO) datasets have been widely researched and discussed. Such studies have developed state-of-the-art methods to map urban features like building footprint or roads in detail. This study delves deeper into combining multiple such studies to identify fine-grained urban features which define streetscapes. Specifically, the research focuses on employing object detection and semantic segmentation models and other computer vision methods to identify ten streetscape features such as movement corridors, roadways, sidewalks, bike paths, on-street parking, vehicles, trees, vegetation, road markings, and buildings. The training data for identifying and classifying all the elements except road markings are collected from open sources and finetuned to fit the study’s context. The training dataset is manually created and employed to delineate road markings. Apart from the model-specific evaluation on the test-set of the data, the study creates its own test dataset from the study area to analyze these models’ performance. The outputs from these models are further integrated to develop a geospatial dataset, which is additionally utilized to generate 3D views and street cross-sections for the city. The trained models and data sources are discussed in the research and are made available for urban researchers to exploit.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs13173363