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Special Issue "Earth Observation and Citizen Contributed Data for Urban Sustainability"

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

Deadline for manuscript submissions: closed (27 October 2023) | Viewed by 7491

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Special Issue Editors

Prof. Dr. Maria Antonia Brovelli

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Guest Editor

Department of Civil and Environmental Engineering, Politecnico di Milano, P.zza Leonardo da Vinci, 32, Building 3, 20133 Milano, Italy
Interests: geospatial web; geodata science; citizen science; open science; open data; open geospatial software; geospatial artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Qi Zhou

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Guest Editor

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: multi-source data fusion; analysis of big data; spatial data deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Andong Ma

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Guest Editor

Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA
Interests: remote sensing image processing; machine learning; deep learning; GeoAI; spatial big data analytics

Special Issue Information

Dear Colleagues,

As the world continues to urbanize, urban sustainability has become the theme of our time. Remote sensing and citizen-contributed data provide critical insight to monitor and achieve Sustainable Development Goals (SDGs). Remote sensing provides essential approaches to monitoring urban evolution at spatial and temporal scales for our understanding of city changes and their influence on natural resources and environmental systems. Coupled with the traditional methods and remote sensing techniques, citizen science has a great potential to provide timely, reliable and comprehensive data for collaborative action. Vast numbers of urban sensors could make cities smarter. The availability of high-performance computing platforms and the development of artificial intelligence (AI) provide new opportunities for the integration of multi-sources information and large-volume data analysis.

This Special Issue invites manuscripts that present new developments and methodologies, practices, and applications related to urban sustainability issues with remote sensing (e.g., high-resolution, multi-spectral, hyperspectral, LiDAR, thermal) and citizen-contributed data (e.g., OSM, social media, file sharing, Internet of Things). Recent advancements in multi-source data integration, multi-scale approaches, big data analysis, data mining, machine learning or studies focused on urban sustainability are welcome. Original research articles, reviews, letters, technical notes, and highlight articles may address, but are not limited to, the following topics:

Remote sensing image processing;
Citizen contributed data analysis;
Multi-source data integration;
Multi-scale approaches;
Big data analysis and data mining;
Machine learning and Earth Observation (citizen contributed data included);
Internet of Things in an urban context;
Digital twin cities;
Geospatial science and techniques for urban sustainability.

Prof. Dr. Maria Antonia Brovelli
Dr. Qi Zhou
Dr. Andong Ma
Guest Editors

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

Earth observation
citizen science
urban sustainability
SDG 11
geomatics and Earth observation machine learning (GEOML)
digital twin cities

Published Papers (5 papers)

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Research

22 pages, 13728 KiB

Open AccessArticle

Mapping Slums in Mumbai, India, Using Sentinel-2 Imagery: Evaluating Composite Slum Spectral Indices (CSSIs)

and

Remote Sens. 2023, 15(19), 4671; https://doi.org/10.3390/rs15194671 - 23 Sep 2023

Viewed by 597

Abstract

Accurate geographic data of slums are important for handling urban poverty issues. Previous slum mapping studies using high-resolution or very-high-resolution (HR/VHR) remotely sensed (RS) images are commonly not suitable for city-wide scale tasks. This study aims to efficiently generate a slum map on a city-wide scale using freely accessed multispectral medium-resolution (MR) Sentinel-2 images. Composite slum spectral indices (CSSIs) were initially proposed based on the shapes of spectral profiles of slums and nonslums and directly represent slum characteristics. Specifically, CSSI-1 denotes the normalized difference between the shortwave infrared bands and the red edge band, while CSSI-2 denotes the normalized difference between the blue band and the green band. Furthermore, two methods were developed to test the effectiveness of CSSIs on slum mapping, i.e., the threshold-based method and the machine learning (ML)-based method. Experimental results show that the threshold-based method and the ML-based method achieve intersection over unions (IoU) of 43.89% and 54.45% in Mumbai, respectively. The accuracies of our methods are comparable to or even higher than the accuracies reported by existing methods using HR/VHR images and transfer learning. The threshold-based method exhibits a promising performance in mapping slums larger than 5 ha, while the ML-based method refines mapping accuracies for slum pockets smaller than 5 ha. The threshold-based method and the ML-based method produced the slum map in Mumbai in 2 and 28 min, respectively. Our methods are suitable for rapid large-area slum mapping owing to the high data availability of Sentinel-2 images and high computational efficiency. Full article

(This article belongs to the Special Issue Earth Observation and Citizen Contributed Data for Urban Sustainability)

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20 pages, 7819 KiB

Open AccessArticle

Fusing Social Media, Remote Sensing, and Fire Dynamics to Track Wildland-Urban Interface Fire

and

Remote Sens. 2023, 15(15), 3842; https://doi.org/10.3390/rs15153842 - 02 Aug 2023

Viewed by 694

Abstract

Wildfire is one of the main hazards affecting large areas and causes great damage all over the world, and the rapid development of the wildland-urban interface (WUI) increases the threat of wildfires that have ecological, social, and economic consequences. As one of the most widely used methods for tracking fire, remote sensing can provide valuable information about fires, but it is not always available, and needs to be supplemented by data from other sources. Social media is an emerging but underutilized data source for emergency management, contains a wealth of disaster information, and reflects the public’s real-time witness and feedback to fires. In this paper, we propose a fusion framework of multi-source data analysis, including social media data and remote sensing data, cellphone signaling data, terrain data, and meteorological data to track WUI fires. Using semantic web technology, the framework has been implemented as a Knowledge Base Service and runs on top of WUIFire ontology. WUIFire ontology represents WUI fire–related knowledge and consists of three modules: system, monitoring, and spread, and tracks wildfires happening in WUIs. It provides a basis for tracking and analyzing a WUI fire by fusing multi-source data. To showcase the utility of our approach in a real-world scenario, we take the fire in the Yaji Mountain Scenic Area, Beijing, China, in 2019 as a case study. With object information identified from remote sensing, fire situation information extracted from Weibo, and fire perimeters constructed through fire spread simulation, a knowledge graph is constructed and an analysis using a semantic query is carried out to realize situational awareness and determine countermeasures. The experimental results demonstrate the benefits of using a semantically improved multi-source data fusion framework for tracking WUI fire. Full article

(This article belongs to the Special Issue Earth Observation and Citizen Contributed Data for Urban Sustainability)

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19 pages, 19495 KiB

Open AccessArticle

A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data

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Remote Sens. 2023, 15(12), 3189; https://doi.org/10.3390/rs15123189 - 19 Jun 2023

Cited by 1 | Viewed by 920

Abstract

Physical urban boundaries (PUBs) are basic geographic information data for defining the spatial extent of urban landscapes with non-agricultural land and non-agricultural economic activities. Accurately mapping PUBs provides a spatiotemporal database for urban dynamic monitoring, territorial spatial planning, and ecological environment protection. However, traditional extraction methods often have problems, such as subjective parameter settings and inconsistent cartographic scales, making it difficult to identify PUBs objectively and accurately. To address these problems, we proposed a self-supervised learning approach for PUB extraction. First, we used nighttime light and OpenStreetMap road data to map the initial urban boundary for data preparation. Then, we designed a pretext task of self-supervised learning based on an unsupervised mutation detection algorithm to automatically mine supervised information in unlabeled data, which can avoid subjective human interference. Finally, a downstream task was designed as a supervised learning task in Google Earth Engine to classify urban and non-urban areas using impervious surface density and nighttime light data, which can solve the scale inconsistency problem. Based on the proposed method, we produced a 30 m resolution China PUB dataset containing six years (i.e., 1995, 2000, 2005, 2010, 2015, and 2020). Our PUBs show good agreement with existing products and accurately describe the spatial extent of urban areas, effectively distinguishing urban and non-urban areas. Moreover, we found that the gap between the national per capita GDP and the urban per capita GDP is gradually decreasing, but regional coordinated development and intensive development still need to be strengthened. Full article

(This article belongs to the Special Issue Earth Observation and Citizen Contributed Data for Urban Sustainability)

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23 pages, 7172 KiB

Open AccessArticle

Insights into the Effect of Urban Morphology and Land Cover on Land Surface and Air Temperatures in the Metropolitan City of Milan (Italy) Using Satellite Imagery and In Situ Measurements

Mathilde Puche

Alberto Vavassori

and

Maria Antonia Brovelli

Remote Sens. 2023, 15(3), 733; https://doi.org/10.3390/rs15030733 - 27 Jan 2023

Cited by 4 | Viewed by 1758

Abstract

With a concentration of people, activities, and infrastructures, urban areas are particularly vulnerable to the negative effects of climate change. Among others, the intensification of the Urban Heat Island (UHI) effect is leading to an increased impact on citizen health and the urban ecosystem. In this context, this study aims to investigate the effect of urban morphology and land cover composition—which are established by exploiting the Local Climate Zone (LCZ) classification system—on two urban climate indicators, i.e., Land Surface Temperature (LST) and air temperature. The study area is the Metropolitan City of Milan (northern Italy). LCZ and LST maps are derived by leveraging satellite imagery and building height datasets. Both authoritative and crowdsourced in situ measurements are used for the analysis of air temperature. Several experiments are run to investigate the mutual relation between LCZ, LST, and air temperature by measuring LST and air temperature patterns in different LCZs and periods. Besides a strong temporal correlation between LST and air temperature, results point out vegetation and natural areas as major mitigating factors of both variables. On the other hand, higher buildings turn out to increase local air temperature while buffering LST values. A way lower influence of building density is measured, with compact building areas experiencing slightly higher air temperature yet no significant differences in terms of LST. These outcomes provide valuable tools to urban planners and stakeholders for implementing evidence-based UHI mitigation strategies. Full article

(This article belongs to the Special Issue Earth Observation and Citizen Contributed Data for Urban Sustainability)

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16 pages, 2989 KiB

Open AccessArticle

Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space Mapping

Qi Zhou

and

Xuanqiao Jing

Remote Sens. 2022, 14(22), 5764; https://doi.org/10.3390/rs14225764 - 15 Nov 2022

Cited by 2 | Viewed by 1383

Abstract

Blue spaces (or water bodies) have a positive impact on the built-up environment and human health. Various open and high-resolution land-use/land-cover (LULC) datasets may be used for mapping blue space, but they have rarely been quantitatively evaluated and compared. Moreover, few studies have investigated whether existing 10-m-resolution LULC datasets can identify water bodies with widths as narrow as 10 m. To fill these gaps, this study evaluates and compares four LULC datasets (ESRI, ESA, FROM-GLC10, OSM) for blue space mapping in Great Britain. First, a buffer approach is proposed for the extraction of water bodies of different widths from a reference dataset. This approach is applied to each LULC dataset, and the results are compared in terms of accuracy, precision, recall, and the F1-score. We find that a high median accuracy (i.e., >98%) is achieved with all four LULC datasets. The OSM dataset gives the best recall and F1-score. Both the ESRI and ESA datasets produce better results than the FORM-GLC10 dataset. Additionally, the OSM dataset enables the identification of water bodies with widths of 10 m, whereas only water bodies with widths of 20 m or more can be identified in the other datasets. These findings may be beneficial for urban planners and designers in selecting an appropriate LULC dataset for blue space mapping. Full article

(This article belongs to the Special Issue Earth Observation and Citizen Contributed Data for Urban Sustainability)

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