Special Issue "Intelligent Perception in Urban Spaces from Photogrammetry and 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: 31 August 2022 | Viewed by 2805

Special Issue Editors

Dr. Jiaming Na
E-Mail Website1 Website2
Guest Editor
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: image- and LiDAR-based segmentation and reconstruction; UAV photogrammetry and digital terrain analysis and related photogrammetry and remote sensing applications in the field of civil engineering; land use change and geomorphology
Dr. Hu Ding
E-Mail Website
Guest Editor
School of Geography, South China Normal University, Guangzhou 510631, China
Interests: object-based image analysis and deep learning for geomorphometry and urban remote sensing applications
Prof. Dr. Yingchun Fu
E-Mail Website
Guest Editor
School of Geography, South China Normal University, Guangzhou 510631, China
Interests: high-resolution time series remote sensing; intelligent image analysis and geocomputation; geospatial information sciences
Prof. Dr. Fang Qiu
E-Mail Website
Guest Editor
School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA
Interests: digital processing of emerging remotely sensed data, such as those collected by LiDAR; hyperspectral and high-spatial-resolution sensors; spatial analysis and modeling; high-performance geocomputation

Special Issue Information

Dear Colleagues,

Cities play an important role in global society and the economy, as well as the environment as a whole. With the recent development of photogrammetry and remote sensing, more and more recent technologies are being introduced to urban studies and new methodologies, which take advantage of these technologies, are being proposed. For example, object-based image analysis (OBIA), machine/deep learning and time series analysis, and other new methodologies significantly improved the degree of intelligence in urban studies. While the achievements of these early works laid a great foundation through proof-of-concept studies, we are now in a position to instill intelligent perceptions in every practical aspect of urban spaces.

To achieve this, there remain a number of challenges. These include practical issues, such as designing new devices or frameworks for data collection, enhancing current object-based image analysis methods for high-resolution data, adapting new machine/deep learning methods for the specifics of urban studies, and developing other new methods to better understand urban processes. Although proof-of-concept work is still necessary, original research with practical applications must be proposed and carried out to enrich the current state of the art.

In this issue, we welcome all novel urban studies that deploy remote sensing or photogrammetric technologies to achieve intelligent perception in urban spaces. We intend to cover all practical aspects ranging from data generation, data processing, spatial analysis and statistics, and innovative applications. Potential topics include, but are not limited to, 3D city morphology, city renewal, land use/land cover (LULC) and change detection, urban sprawl modelling, urban heat islands response, urban rainstorm waterlogging risk assessment, and multiple-source, data-based urban fringe recognition. Both original research papers and reviews with unique scientific insights are welcome. This Special Issue will be a comprehensive collection of articles, which reflect the current research progress in urban studies.

Dr. Jiaming Na
Dr. Hu Ding
Prof. Dr. Yingchun Fu
Prof. Dr. Fang Qiu
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 2500 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

  • photogrammetry
  • object-based image analysis (OBIA)
  • time series analysis
  • deep learning
  • machine learning
  • spatial temporal change detection
  • urban renewal
  • 3D urban morphology
  • land use/land cover (LULC)

Published Papers (3 papers)

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Research

Article
DGS-SLAM: A Fast and Robust RGBD SLAM in Dynamic Environments Combined by Geometric and Semantic Information
Remote Sens. 2022, 14(3), 795; https://doi.org/10.3390/rs14030795 - 08 Feb 2022
Cited by 2 | Viewed by 829
Abstract
Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to accomplish fully autonomous movement and exploration in unknown environments. At present, many impressive VSLAM systems have emerged, but most of them rely on the static world assumption, which limits their application [...] Read more.
Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to accomplish fully autonomous movement and exploration in unknown environments. At present, many impressive VSLAM systems have emerged, but most of them rely on the static world assumption, which limits their application in real dynamic scenarios. To improve the robustness and efficiency of the system in dynamic environments, this paper proposes a dynamic RGBD SLAM based on a combination of geometric and semantic information (DGS-SLAM). First, a dynamic object detection module based on the multinomial residual model is proposed, which executes the motion segmentation of the scene by combining the motion residual information of adjacent frames and the potential motion information of the semantic segmentation module. Second, a camera pose tracking strategy using feature point classification results is designed to achieve robust system tracking. Finally, according to the results of dynamic segmentation and camera tracking, a semantic segmentation module based on a semantic frame selection strategy is designed for extracting potential moving targets in the scene. Extensive evaluation in public TUM and Bonn datasets demonstrates that DGS-SLAM has higher robustness and speed than state-of-the-art dynamic RGB-D SLAM systems in dynamic scenes. Full article
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Article
Fine-Scale Improved Carbon Bookkeeping Model Using Landsat Time Series for Subtropical Forest, Southern China
Remote Sens. 2022, 14(3), 753; https://doi.org/10.3390/rs14030753 - 06 Feb 2022
Viewed by 498
Abstract
Subtropical forests easily suffer anthropogenic disturbance, including deforestation and reforestation management, which both highly affect the carbon pools. This study proposes spatial-temporal tracking of the carbon density dynamics to improve bookkeeping in the carbon model and applied to subtropical forest activities in Guangzhou, [...] Read more.
Subtropical forests easily suffer anthropogenic disturbance, including deforestation and reforestation management, which both highly affect the carbon pools. This study proposes spatial-temporal tracking of the carbon density dynamics to improve bookkeeping in the carbon model and applied to subtropical forest activities in Guangzhou, southern China, during the period of 1995 to 2014. Based on the overall accuracy of 87.5% ± 1.7% for forest change products using Landsat time series (LTS), we found that this is a typical period of deforestation conversion to reforestation activity accompanied with urbanization. Additionally, linear regression, random forest regression and allometric growth fitting were proposed by using forest field plots to obtain reliable per-pixel carbon density estimations. The cross-validation (CV) of random forest with LTS-derived parameters reached the highest accuracy of R2 and RMSE of 0.763 and 7.499 Mg ha−1. The RMES of the density estimation ranged between 78 and 84% of the mean observed biomass in the study area, which outperformed previous studies. Over the 20-year period, the study results showed that the explicit carbon emissions were (6.82 ± 0.26) × 104 Mg C yr−1 from deforestation; emissions increased to (1.02 ± 0.04) × 105 Mg C yr−1 given the implicit carbon not yet released to the atmosphere in the form of decomposing slash and wood products. In addition, a carbon uptake of about 1.91 ± 0.73 × 105 Mg C yr−1, presented as the net carbon pool. Based on the continuous detection capability, biennial reforestation activity has increased carbon density by a growth rate of 1.55 Mg ha−1, and the emission factors can be identified with LTS-derived parameters. In general, the study realizes the spatiotemporal improvement of carbon density and flux dynamics tracking, including the abrupt and graduate change based on fine-scale forest activity. It can provide more comprehensive and detailed feedback on the carbon source and sink change process of forest activities and disturbances. Full article
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Article
Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models
Remote Sens. 2022, 14(3), 599; https://doi.org/10.3390/rs14030599 - 26 Jan 2022
Cited by 2 | Viewed by 869
Abstract
Due to the country’s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is [...] Read more.
Due to the country’s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city. Full article
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