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Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 33301

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Special Issue Editors

Dr. Tania Stathaki

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Guest Editor

Communications and Signal Processing Research Group, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Interests: image fusion; computer vision; remote sensing; urban monitoring; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Barmpoutis Panagiotis

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Guest Editor

Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece
Interests: forest informatics; forest monitoring; natural hazards detection and assessment; computer vision; pattern analysis; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The environmental challenges the world faces nowadays have never been greater or more complex. More specifically, environmental problems such as climate change, wildfires, soil and water pollution, geohazards, biodiversity loss, land degradation, and desertification are becoming increasingly frequent and more extreme.

This means we need to take measures to tackle the above challenges mitigating their effects in an operationally, time and power cost efficient manner, using Computer Vision and Machine Learning for Remote Sensing novel methodologies. Indeed, recent advances on remote sensing technologies have led to a dramatic increase in the types of signals and imagery available. Moreover, the wide variety of different sensors in combination with the modern signal and image processing, computer vision and machine learning enable the near real-time environmental data acquisition, assessment, processing and analysis for the ultimate goals of ecosystem protection and monitoring. To this end, this Special Issue entitled “Computer Vision and Machine Learning for Remote Sensing Solutions applied to Environmental Challenges” is focused on the urgent priority around protecting the value and potential of the ecosystem and global future.

Contributions may be of many different kinds, ranging from research and application-oriented papers describing innovative signal and image processing, computer vision and machine learning applied to remote sensing solutions for effectively addressing environmental challenges, to more theoretical studies discussing recommendations for more effective solutions for these challenges now and into the future.

Dr. Tania Stathaki
Dr. Panagiotis Barmpoutis
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

remote sensing
environmental challenges
climate change
monitoring
protection
image and signal processing
machine learning
deep learning

Published Papers (9 papers)

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Research

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19 pages, 11857 KiB

Open AccessArticle

Suburban Forest Fire Risk Assessment and Forest Surveillance Using 360-Degree Cameras and a Multiscale Deformable Transformer

by Panagiotis Barmpoutis, Aristeidis Kastridis, Tania Stathaki, Jing Yuan, Mengjie Shi and Nikos Grammalidis

Remote Sens. 2023, 15(8), 1995; https://doi.org/10.3390/rs15081995 - 10 Apr 2023

Cited by 7 | Viewed by 2296

Abstract

In the current context of climate change and demographic expansion, one of the phenomena that humanity faces are the suburban wildfires. To prevent the occurrence of suburban forest fires, fire risk assessment and early fire detection approaches need to be applied. Forest fire risk mapping depends on various factors and contributes to the identification and monitoring of vulnerable zones where risk factors are most severe. Therefore, watchtowers, sensors, and base stations of autonomous unmanned aerial vehicles need to be placed carefully in order to ensure adequate visibility or battery autonomy. In this study, fire risk assessment of an urban forest was performed and the recently introduced 360-degree data were used for early fire detection. Furthermore, a single-step approach that integrates a multiscale vision transformer was introduced for accurate fire detection. The study area includes the suburban pine forest of Thessaloniki city (Greece) named Seich Sou, which is prone to wildfires. For the evaluation of the performance of the proposed workflow, real and synthetic 360-degree images were used. Experimental results demonstrate the great potential of the proposed system, which achieved an F-score for real fire event detection rate equal to 91.6%. This indicates that the proposed method could significantly contribute to the monitoring, protection, and early fire detection of the suburban forest of Thessaloniki. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges)

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21 pages, 29641 KiB

Open AccessArticle

An Improved YOLOv5 Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images

by Zhenhui Sun, Peihang Li, Qingyan Meng, Yunxiao Sun and Yaxin Bi

Remote Sens. 2023, 15(7), 1796; https://doi.org/10.3390/rs15071796 - 28 Mar 2023

Cited by 7 | Viewed by 2271

Abstract

Tailings ponds’ failure and environmental pollution make tailings monitoring very important. Remote sensing technology can quickly and widely obtain ground information and has become one of the important means of tailings monitoring. However, the efficiency and accuracy of traditional remote sensing monitoring technology have difficulty meeting the management needs. At the same time, affected by factors such as the geographical environment and imaging conditions, tailings have various manifestations in remote sensing images, which all bring challenges to the accurate acquisition of tailings information in large areas. By improving You Only Look Once (YOLO) v5s, this study designs a deep learning-based framework for the large-scale extraction of tailings ponds information from the entire high-resolution remote sensing images. For the improved YOLOv5s, the Swin Transformer is integrated to build the Swin-T backbone, the Fusion Block of efficient Reparameterized Generalized Feature Pyramid Network (RepGFPN) in DAMO-YOLO is introduced to form the RepGFPN Neck, and the head is replaced with Decoupled Head. In addition, sample boosting strategy (SBS) and global non-maximum suppression (GNMS) are designed to improve the sample quality and suppress repeated detection frames in the entire image, respectively. The model test results based on entire Gaofen-6 (GF-6) high-resolution remote sensing images show that the F1 score of tailings ponds is significantly improved by 12.22% compared with YOLOv5, reaching 81.90%. On the basis of both employing SBS, the improved YOLOv5s boots the [email protected] of YOLOv5s by 5.95%, reaching 92.15%. This study provides a solution for tailings ponds’ monitoring and ecological environment management. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges)

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20 pages, 26371 KiB

Open AccessArticle

A Hierarchical Information Extraction Method for Large-Scale Centralized Photovoltaic Power Plants Based on Multi-Source Remote Sensing Images

by Fan Ge, Guizhou Wang, Guojin He, Dengji Zhou, Ranyu Yin and Lianzi Tong

Remote Sens. 2022, 14(17), 4211; https://doi.org/10.3390/rs14174211 - 26 Aug 2022

Cited by 12 | Viewed by 2101

Abstract

In the context of global sustainable development, solar energy is very widely used. The installed capacity of photovoltaic panels in countries around the world, especially in China, is increasing steadily and rapidly. In order to obtain accurate information about photovoltaic panels and provide data support for the macro-control of the photovoltaic industry, this paper proposed a hierarchical information extraction method, including positioning information and shape information, and carried out photovoltaic panel distribution mapping. This method is suitable for large-scale centralized photovoltaic power plants based on multi-source satellite remote sensing images. This experiment takes the three northwest provinces of China as the research area. First, a deep learning scene classification model, the EfficientNet-B5 model, is used to locate the photovoltaic power plants on 16-m spatial resolution images. This step obtains the area that contains or may contain photovoltaic panels, greatly reducing the study area with an accuracy of 99.97%. Second, a deep learning semantic segmentation model, the U²-Net model, is used to precisely locate photovoltaic panels on 2-m spatial resolution images. This step achieves the exact extraction results of the photovoltaic panels from the area obtained in the previous step, with an accuracy of 97.686%. This paper verifies the superiority of a hierarchical information extraction method in terms of accuracy and efficiency through comparative experiments with DeepLabV3+, U-Net, SegNet, and FCN8s. This meaningful work identified 180 centralized photovoltaic power plants in the study area. Additionally, this method makes full use of the characteristics of different remote sensing data sources. This method can be applied to the rapid extraction of global photovoltaic panels. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges)

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17 pages, 11292 KiB

Open AccessArticle

An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery

by Yutong Jia, Lei Liu, Siqing Peng, Mingyang Feng and Gang Wan

Remote Sens. 2022, 14(6), 1391; https://doi.org/10.3390/rs14061391 - 13 Mar 2022

Cited by 3 | Viewed by 2196

Abstract

Lunar craters and rilles are significant topographic features on the lunar surface that will play an essential role in future research on space energy resources and geological evolution. However, previous studies have shown low efficiency in detecting lunar impact craters and poor accuracy in detecting lunar rilles. There is no complete automated identification method for lunar features to explore space energy resources further. In this paper, we propose a new specific deep-learning method called high-resolution global–local networks (HR-GLNet) to explore craters and rilles and to discover space energy simultaneously. Based on the GLNet network, the ResNet structure in the global branch is replaced by HRNet, and the residual network and FPN are the local branches. Principal loss function and auxiliary loss function are used to aggregate global and local branches. In experiments, the model, combined with transfer learning methods, can accurately detect lunar craters, Mars craters, and lunar rilles. Compared with other networks, such as UNet, ERU-Net, HRNet, and GLNet, GL-HRNet has a higher accuracy (88.7 ± 8.9) and recall rate (80.1 ± 2.7) in lunar impact crater detection. In addition, the mean absolute error (MAE) of the GL-HRNet on global and local branches is 0.0612 and 0.0429, which are better than the GLNet in terms of segmentation accuracy and MAE. Finally, by analyzing the density distribution of lunar impact craters with a diameter of less than 5 km, it was found that: (i) small impact craters in a local area of the lunar north pole and highland (5°–85°E, 25°–50°S) show apparent high density, and (ii) the density of impact craters in the Orientale Basin is not significantly different from that in the surrounding areas, which is the direction for future geological research. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges)

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24 pages, 200799 KiB

Open AccessArticle

Geographical Detection of Urban Thermal Environment Based on the Local Climate Zones: A Case Study in Wuhan, China

by Renfeng Wang, Mengmeng Wang, Zhengjia Zhang, Tian Hu, Jiawen Xing, Zhanjun He and Xiuguo Liu

Remote Sens. 2022, 14(5), 1067; https://doi.org/10.3390/rs14051067 - 22 Feb 2022

Cited by 18 | Viewed by 2680

Abstract

The urban morphology has impacts on the urban thermal environment, which has drawn extensive attention, especially in metropolitan regions with intensive populations and high building densities. This study explored the relationship between the urban morphology and spatial variation of land surface temperature (LST) in Wuhan by using the local climate zone (LCZ) and seven natural and social factors. A deep learning model (light LCZ model) was used to generate LCZ map in Wuhan, and a geographic detector model was utilized to explore the driving mechanism of LST spatial differentiation. The results show that the LST difference between LCZ classes in summer is greater than that in winter, and the LST of the built-up classes (LCZ 1–10) are significantly higher than that of the vegetation classes in summer. Among the six residential building classes (i.e., LCZ 1–6), LCZ 1 is characterized by compact and high buildings and has the largest average LST. Building density and height have a warming effect, and the building density has a stronger effect than the height. Compared with other natural and social factors, LCZ has the largest explanatory power for LST spatial differentiation in the main urban area and surrounding areas of Wuhan, with explanatory power (q) values reaching 0.660 (summer) and 0.316 (winter). The types of interaction for all pairwise cases are mutual and nonlinear. The strongest interaction is MNDWI-NDBI combination (0.780) in summer and LCZ-NDBI combination (0.460) in winter. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges)

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17 pages, 2016 KiB

Open AccessArticle

Rapid Identification and Prediction of Cadmium-Lead Cross-Stress of Different Stress Levels in Rice Canopy Based on Visible and Near-Infrared Spectroscopy

by Shuangyin Zhang, Jun Li, Siying Wang, Yingjing Huang, Yizhuo Li, Yiyun Chen and Teng Fei

Remote Sens. 2020, 12(3), 469; https://doi.org/10.3390/rs12030469 - 02 Feb 2020

Cited by 12 | Viewed by 3048

Abstract

Accurate detection of cadmium (Cd) and lead (Pb)-induced cross-stress on crops is essential for agricultural, ecological environment, and food security. The feasibility to diagnose and predict Cd–Pb cross-stress in agricultural soil was explored by measuring the visible and near-infrared reflectance of rice leaves. In this study, two models were developed—namely a diagnostic model and a prediction model. The diagnostic model was established based on visible and near-infrared reflectance spectroscopy (VNIRS) datasets with Support Vector Machine (SVM), followed by leave-one-out cross-validation (LOOCV). A partial least-squares (PLS) regression, as the prediction model was employed to predict the foliar concentration of Cd and Pb contents. To accurately calibrate the two models, a rigorous greenhouse experiment was designed and implemented, with 4 levels of treatments on each of the Cd and Pb stress on rice. Results show that with the appropriate pre-processing, the diagnostic model can identify 79% of Cd and 85% of Pb stress of any levels. The significant bands that have been used mainly distributed between 681–776 nm and 1224–1349 nm for Cd stress and 712–784 nm for Pb stress. The prediction model can estimate Cd with coefficient of determination of 0.7, but failed to predict Pb accurately. The results illustrated the feasibility to diagnose Cd stress accurately by measuring the visible and near-infrared reflectance of rice canopy in a cross-contamination soil environment. This study serves as one step forward to heavy metal pollutant detection in a farmland environment. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges)

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Review

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28 pages, 2994 KiB

Open AccessReview

Open-Source Data Alternatives and Models for Flood Risk Management in Nepal

by Sudeep Thakuri, Binod Prasad Parajuli, Puja Shakya, Preshika Baskota, Deepa Pradhan and Raju Chauhan

Remote Sens. 2022, 14(22), 5660; https://doi.org/10.3390/rs14225660 - 09 Nov 2022

Cited by 1 | Viewed by 3000

Abstract

Availability and applications of open-source data for disaster risk reductions are increasing. Flood hazards are a constant threat to local communities and infrastructures (e.g., built-up environment and agricultural areas) in Nepal. Due to its negative consequences on societies and economic aspects, it is critical to monitor and map those risks. This study presents the open access earth observation (EO) data, geospatial products, and different analytical models available for flood risk assessment (FRA) and monitoring in Nepal. The status of flood risk knowledge and open-source data was reviewed through a systematic literature review. Multispectral optical data are widely used, but use of microwave data is extremely low. With the recent developments in this field, especially optical and microwave data, the monitoring, mapping, and modeling of flood hazards and risk have been more rapid and precise and are published in several scientific articles. This study shows that the choice of appropriate measurements and data for a flood risk assessment and management involves an understanding of the flood risk mechanism, flood plain dynamics, and primary parameter that should be addressed in order to minimize the risk. At the catchments, floodplains, and basin level, a variety of open data sources and models may be used under different socioeconomic and environmental limitations. If combined and analyzed further, multi-source data from different models and platforms could produce a new result to better understand the risks and mitigation measures related to various disasters. The finding of this study helps to select and apply appropriate data and models for flood risk assessment and management in the countries like Nepal where the proprietary data and models are not easily accessible. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges)

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25 pages, 726 KiB

Open AccessReview

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

by Teo Nguyen, Benoît Liquet, Kerrie Mengersen and Damien Sous

Remote Sens. 2021, 13(21), 4470; https://doi.org/10.3390/rs13214470 - 07 Nov 2021

Cited by 8 | Viewed by 6711

Abstract

Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges)

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Other

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17 pages, 47052 KiB

Open AccessTechnical Note

Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures

by Panagiotis Barmpoutis, Tania Stathaki, Kosmas Dimitropoulos and Nikos Grammalidis

Remote Sens. 2020, 12(19), 3177; https://doi.org/10.3390/rs12193177 - 28 Sep 2020

Cited by 65 | Viewed by 6795

Abstract

The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire detection, making it possible to obtain unlimited field of view captures which reduce the number of required sensors and the computational cost and make the systems more efficient. More specifically, once optical 360-degree raw data are obtained using an RGB 360-degree camera mounted on an unmanned aerial vehicle, we convert the equirectangular projection format images to stereographic images. Then, two DeepLab V3+ networks are applied to perform flame and smoke segmentation, respectively. Subsequently, a novel post-validation adaptive method is proposed exploiting the environmental appearance of each test image and reducing the false-positive rates. For evaluating the performance of the proposed system, a dataset, namely the “Fire detection 360-degree dataset”, consisting of 150 unlimited field of view images that contain both synthetic and real fire, was created. Experimental results demonstrate the great potential of the proposed system, which has achieved an F-score fire detection rate equal to 94.6%, hence reducing the number of required sensors. This indicates that the proposed method could significantly contribute to early fire detection. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Remote Sensing Solutions Applied to Environmental Challenges)

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