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Change Detection and Semantic Characterization of Urban and Rural Environments...
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Change Detection and Semantic Characterization of Urban and Rural Environments Based on Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (16 May 2023) | Viewed by 11668

Special Issue Editor

Dr. Marc Bosch

E-Mail Website
Guest Editor
Accenture, Washington, DC, USA
Interests: image processing; machine learning; computer vision; video coding; computational photography

Special Issue Information

Dear Colleagues,

Each year, Earth-observing satellites generate hundreds of terabytes of data; AI and machine learning are thus needed to accelerate the processing and analysis of these images. With automation, we can determine the speed and scale needed to make the data relevant. In particular, change detection and semantic characterization could enable better monitoring of urban and rural environments and largely impact our society and our planet.

A new wave of image processing, geospatial computer vision and machine learning techniques, can accelerate our understanding of changes occurring on the Earth's surface.

To this end, this Special Issue is seeking papers presenting novel ideas, techniques and tools to improve change detection and semantic characterization. Topics of interest include, but are not limited to: structure detection, semantic segmentation, object identification, 3D representation, land cover change detection, feature extraction and classification, large-scale model generalization and multi-environment adaptation.

Dr. Marc Bosch
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • building detection
  • road detection
  • semantic classification or segmentation of satellite images
  • domain adaptation
  • land cover change
  • land zone identification
  • 3D urban modeling
  • multiview stereo
  • multimodal fusion for change detection
  • public benchmarks: training datasets and evaluation metrics
  • geospatial compute frameworks: large-scale compute pipelines and remote sensing data and algorithms

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

22 pages, 12368 KiB  
Article
A Functional Zoning Method in Rural Landscape Based on High-Resolution Satellite Imagery
by Yuying Zheng, Yuanyong Dian, Zhiqiang Guo, Chonghuai Yao and Xuefei Wu
Remote Sens. 2023, 15(20), 4920; https://doi.org/10.3390/rs15204920 - 12 Oct 2023
Cited by 1 | Viewed by 1894
Abstract
Mapping functional zones for rural landscapes is the foundational work for rural land use planning and plays a very important role in the economic development and resource management utilization of rural areas. However, the traditional manual delineation of functional zone boundaries empirically in [...] Read more.
Mapping functional zones for rural landscapes is the foundational work for rural land use planning and plays a very important role in the economic development and resource management utilization of rural areas. However, the traditional manual delineation of functional zone boundaries empirically in rural areas is labor-intensive, time-consuming, and lacks the consideration of spatial landscape patterns. The emergence of high-resolution remote sensing imagery and image segmentation has facilitated the analysis of ground landscape information and patterns, but there is still a lack of functional zone boundary mapping methods applicable to rural landscapes. To address this, we propose a functional zoning method called multiscale merging of landscape contextual and shape characteristics with heterogeneity indices (M2LHI) for mapping geographic boundaries for rural landscapes based on high-resolution remote sensing imagery. The landscape contextual features were first constructed based on the geospatial distances of landscape types, and then, the dominance index and shape index were introduced to quantify the landscape heterogeneity by object-oriented image analysis. Then, the automated merging of adjacent landscape units based on the thresholds of the landscape heterogeneity indices was performed to map the initial zones. The final rural functional zones were defined based on the main function in the zone. The study was carried out in three typical rural landscapes (hilly countryside, flat countryside, and grassland countryside) located in Fujian, Xinjiang, and Inner Mongolia, China, and the freely available Gaofen-2 (GF-2) satellite imagery was used as the data source. We compared the boundaries of mapped functional zones and reference functional zones, and the matching and inclusion ratios of the final functional zones mapped in each case were bigger than 78%, indicating that the M2LHI method has a high ability to map the functional spatial patterns. The overall accuracies of mapping functional zones with different functions were 95.9%, 89.0%, and 92.1% for the respective cases. The results demonstrated that the M2LHI method effectively quantifies landscape heterogeneity and accurately delineates functional zones with different landscape patterns. It can provide a scientific basis for rural planning and management and efficiently draw reasonable geographic boundaries for rural functional zones. Full article
(This article belongs to the Special Issue Change Detection and Semantic Characterization of Urban and Rural Environments Based on Remote Sensing)
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24 pages, 9468 KiB  
Article
Learning Color Distributions from Bitemporal Remote Sensing Images to Update Existing Building Footprints
by Zehui Wang, Yu Meng, Jingbo Chen, Junxian Ma, Anzhi Yue and Jiansheng Chen
Remote Sens. 2022, 14(22), 5851; https://doi.org/10.3390/rs14225851 - 18 Nov 2022
Viewed by 3308
Abstract
For most cities, municipal governments have constructed basic building footprint datasets that need to be updated regularly for the management and monitoring of urban development and ecology. Cities are capable of changing in a short period of time, and the area of change [...] Read more.
For most cities, municipal governments have constructed basic building footprint datasets that need to be updated regularly for the management and monitoring of urban development and ecology. Cities are capable of changing in a short period of time, and the area of change is variable; hence, automated methods for generating up-to-date building footprints are urgently needed. However, the labels of current buildings or changed areas are usually lacking, and the conditions for acquiring images from different periods are not perfectly consistent, which can severely limit deep learning methods when attempting to learn deep information about buildings. In addition, common update methods can ignore the strictly accurate historical labels of unchanged areas. To solve the above problem, we propose a new update algorithm to update the existing building database to the current state without manual relabeling. First, the difference between the data distributions of different time-phase images is reduced using the image color translation method. Then, a semantic segmentation model predicts the segmentation results of the images from the latest period, and, finally, a post-processing update strategy is applied to strictly retain the existing labels of unchanged regions to attain the updated results. We apply the proposed algorithm on the Wuhan University change detection dataset and the Beijing Huairou district land survey dataset to evaluate the effectiveness of the method in building surface and complex labeling scenarios in urban and suburban areas. The F1 scores of the updated results obtained for both datasets reach more than 96%, which proves the applicability of our proposed algorithm and its ability to efficiently and accurately extract building footprints in real-world scenarios. Full article
(This article belongs to the Special Issue Change Detection and Semantic Characterization of Urban and Rural Environments Based on Remote Sensing)
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18 pages, 3517 KiB  
Article
MECA-Net: A MultiScale Feature Encoding and Long-Range Context-Aware Network for Road Extraction from Remote Sensing Images
by Yongshi Jie, Hongyan He, Kun Xing, Anzhi Yue, Wei Tan, Chunyu Yue, Cheng Jiang and Xuan Chen
Remote Sens. 2022, 14(21), 5342; https://doi.org/10.3390/rs14215342 - 25 Oct 2022
Cited by 19 | Viewed by 2337
Abstract
Road extraction from remote sensing images is significant for urban planning, intelligent transportation, and vehicle navigation. However, it is challenging to automatically extract roads from remote sensing images because the scale difference of roads in remote sensing images varies greatly, and slender roads [...] Read more.
Road extraction from remote sensing images is significant for urban planning, intelligent transportation, and vehicle navigation. However, it is challenging to automatically extract roads from remote sensing images because the scale difference of roads in remote sensing images varies greatly, and slender roads are difficult to identify. Moreover, the road in the image is often blocked by the shadows of trees and buildings, which results in discontinuous and incomplete extraction results. To solve the above problems, this paper proposes a multiscale feature encoding and long-range context-aware network (MECA-Net) for road extraction. MECA-Net adopts an encoder–decoder structure and contains two core modules. One is the multiscale feature encoding module, which aggregates multiscale road features to improve the recognition ability of slender roads. The other is the long-range context-aware module, which consists of the channel attention module and the strip pooling module, and is used to obtain sufficient long-range context information from the channel dimension and spatial dimension to alleviate road occlusion. Experimental results on the open DeepGlobe road dataset and Massachusetts road dataset indicate that the proposed MECA-Net outperforms the other eight mainstream networks, which verifies the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Change Detection and Semantic Characterization of Urban and Rural Environments Based on Remote Sensing)
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16 pages, 3821 KiB  
Article
Machine-Learning-Based Change Detection of Newly Constructed Areas from GF-2 Imagery in Nanjing, China
by Shuting Zhou, Zhen Dong and Guojie Wang
Remote Sens. 2022, 14(12), 2874; https://doi.org/10.3390/rs14122874 - 15 Jun 2022
Cited by 9 | Viewed by 3228
Abstract
Change detection of the newly constructed areas (NCAs) is important for urban development. The advances of remote sensing and deep learning algorithms promotes the high precision of the research work. In this study, we firstly constructed a high-resolution labels for change detection based [...] Read more.
Change detection of the newly constructed areas (NCAs) is important for urban development. The advances of remote sensing and deep learning algorithms promotes the high precision of the research work. In this study, we firstly constructed a high-resolution labels for change detection based on the GF-2 satellite images, and then applied five deep learning models of change detection, including STANets (BASE, BAM, and PAM), SNUNet (Siam-NestedUNet), and BiT (Bitemporal image Transformer) in the Core Region of Jiangbei New Area of Nanjing, China. The BiT model is based on transformer, and the others are based on CNN (Conventional Neural Network). Experiments have revealed that the STANet-PAM model generally performs the best in detecting the NCAs, and the STANet-PAM model can obtain more detailed information of land changes owing to its pyramid spatial-temporal attention module of multiple scales. At last, we have used the five models to analyze urbanization processes from 2015 to 2021 in the study area. Hopefully, the results of this study could be a momentous reference for urban development planning. Full article
(This article belongs to the Special Issue Change Detection and Semantic Characterization of Urban and Rural Environments Based on Remote Sensing)
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