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Machine Learning and GeoAI for Remote Sensing Environmental Monitoring
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Machine Learning and GeoAI for Remote Sensing Environmental Monitoring

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 6375

Special Issue Editor

Prof. Dr. Wenwen Li

E-Mail Website
Guest Editor
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287-5302, USA
Interests: cyberinfrastructure; GeoAI; data science; semantic interoperability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, Geospatial Artificial Intelligence (GeoAI) has become the focus of a new wave of data-driven analytics. This emerging technology has benefited from recent advances in deep machine learning and high-performance computing, and has the capability to process large volumes of environmental data to support knowledge discovery at scale. In just a few years, we have seen a flourishing of research projects and publications in this area, introducing exciting new techniques ranging from analysis of remotely sensed data to detect land use and land cover change, to estimate the rate and extent of permafrost thawing in the Arctic, to identify the formation and intensification of extreme climate events, to model the spread of wildfires, and so on. These techniques have played a key role in understanding the ever-evolving Earth and environmental systems found in the real world, as well as the interactions between them. 

This Special Issue looks for cutting-edge GeoAI and machine learning solutions for environmental monitoring and intelligent analytics. The methods are not limited to supervised or unsupervised machine learning, and we strongly encourage submissions that develop novel (training) datasets, deep learning methods, and workflows to solve environmental problems at a scale, granularity, and speed that has not been possible before. We also encourage critical commentary papers addressing important concerns of GeoAI research, such as model interpretability, reproducibility, transferability, equity, and transparency. 

Topics of interests include, but are not limited to:

  • GeoAI for environmental monitoring;
  • Transparency in GeoAI;
  • Explainable GeoAI;
  • Ethical GeoAI;
  • Reproducibility and replicability of GeoAI;
  • Propagation of data uncertainty through GeoAI;
  • Real-time GeoAI;
  • Next-generation GeoAI models;
  • What makes an AI application “GeoAI”? 

All of the submitted papers should work to solve an environmental problem, such as global warming, ocean acidification, loss of diversity, deforestation, pollution, and overpopulation. 

Prof. Dr. Wenwen Li
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

  • geospatial artificial intelligence
  • environmental intelligence
  • deep learning
  • machine learning
  • explainable GeoAI
  • ethical GeoAI

Published Papers (4 papers)

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Research

26 pages, 5053 KiB  
Article
Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data
by Trevor C. Vannoy, Nathaniel B. Sweeney, Joseph A. Shaw and Bradley M. Whitaker
Remote Sens. 2023, 15(24), 5634; https://doi.org/10.3390/rs15245634 - 05 Dec 2023
Viewed by 1159
Abstract
Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully [...] Read more.
Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully used for detecting and classifying insects. However, the data produced by these lidar systems create several problems from a data analysis standpoint: the data can contain millions of observations, very few observations contain insects, and the background environment is non-stationary. This study compares the insect-detection performance of various supervised machine learning and unsupervised changepoint detection algorithms and provides commentary on the relative strengths of each method. We found that the supervised methods generally perform better than the changepoint detection methods, at the cost of needing labeled data. The supervised learning method with the highest Matthew’s Correlation Coefficient score on the testing set correctly identified 99.5% of the insect-containing images and 83.7% of the non-insect images; similarly, the best changepoint detection method correctly identified 83.2% of the insect-containing images and 84.2% of the non-insect images. Our results show that both types of methods can reduce the need for manual data analysis. Full article
(This article belongs to the Special Issue Machine Learning and GeoAI for Remote Sensing Environmental Monitoring)
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16 pages, 5534 KiB  
Article
Combining the Back Propagation Neural Network and Particle Swarm Optimization Algorithm for Lithological Mapping in North China
by Yanqi Dong, Zhibin Ma, Fu Xu, Xiaohui Su and Feixiang Chen
Remote Sens. 2023, 15(17), 4134; https://doi.org/10.3390/rs15174134 - 23 Aug 2023
Viewed by 755
Abstract
Lithological mapping is a crucial tool for exploring minerals, reconstructing geological formations, and interpreting geological evolution. The study aimed to investigate the application of the back propagation neural network (BPNN) and particle swarm optimization (PSO) algorithm in lithological mapping. The study area is [...] Read more.
Lithological mapping is a crucial tool for exploring minerals, reconstructing geological formations, and interpreting geological evolution. The study aimed to investigate the application of the back propagation neural network (BPNN) and particle swarm optimization (PSO) algorithm in lithological mapping. The study area is the Beiliutumiao map-sheet (No. K49E011021) in Inner Mongolia, China. This area was divided into two parts, with the left side used for training and the right side used for validation. Fifteen geological relevant factors, including geochemistry (1:200,000-scale) and geophysics (1:50,000-scale), were used as predictor variables. Taking one lithology as an example, the lithological binary mapping method was introduced in detail, and then the complete lithology was mapped. The model was compared with commonly used spatial data mining methods using the E-measure, S-measure, and Weighted F-measure values. In diorite testing, the accuracy and kappa of the optimized model were 92.11% and 0.81, respectively. The validation results showed that our method outperformed the traditional BPNN and weights-of-evidence approaches. In the extension of the complete lithological mapping, the accuracy, recall, and F1-score were 82.66%, 74.54%, and 0.76, respectively. Thus, the proposed method is useful for predicting the distribution of one lithology and completing the whole lithological mapping at a fine scale. In addition, the trained network can be extended to an adjacent area with similar lithological features. Full article
(This article belongs to the Special Issue Machine Learning and GeoAI for Remote Sensing Environmental Monitoring)
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18 pages, 6886 KiB  
Article
A Depth-Wise Separable U-Net Architecture with Multiscale Filters to Detect Sinkholes
by Rasha Alshawi, Md Tamjidul Hoque and Maik C. Flanagin
Remote Sens. 2023, 15(5), 1384; https://doi.org/10.3390/rs15051384 - 28 Feb 2023
Cited by 2 | Viewed by 1864
Abstract
Numerous variants of the basic deep segmentation model—U-Net—have emerged in recent years, achieving reliable performance across different benchmarks. In this paper, we propose an improved version of U-Net with higher performance and reduced complexity. This improvement was achieved by introducing a sparsely connected [...] Read more.
Numerous variants of the basic deep segmentation model—U-Net—have emerged in recent years, achieving reliable performance across different benchmarks. In this paper, we propose an improved version of U-Net with higher performance and reduced complexity. This improvement was achieved by introducing a sparsely connected depth-wise separable block with multiscale filters, enabling the network to capture features of different scales. The use of depth-wise separable convolution significantly reduces the number of trainable parameters, making the training faster, while reducing the risk of overfitting. We used our developed sinkhole dataset and the available benchmark nuclei dataset to assess the proposed model’s performance. Pixel-wise annotation is laborious and requires a great deal of human expertise; therefore, we propose a fully deep convolutional autoencoder network that utilizes the proposed block to automatically annotate the sinkhole dataset. Our segmentation model outperformed the state-of-the-art methods, including U-Net, Attention U-Net, Depth-Separable U-Net, and Inception U-Net, achieving an average improvement of 1.2% and 1.4%, respectively, on the sinkhole and the nuclei datasets, with 94% and 92% accuracy, as well as a reduced training time. It also achieved 83% and 80% intersection-over-union (IoU) on the two datasets, respectively, which is an 11.8% and 9.3% average improvement over the above-mentioned models. Full article
(This article belongs to the Special Issue Machine Learning and GeoAI for Remote Sensing Environmental Monitoring)
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20 pages, 12263 KiB  
Article
Automatic Extraction of Marine Aquaculture Zones from Optical Satellite Images by R3Det with Piecewise Linear Stretching
by Yujie Ma, Xiaoyu Qu, Cixian Yu, Lianhui Wu, Peng Zhang, Hengda Huang, Fukun Gui and Dejun Feng
Remote Sens. 2022, 14(18), 4430; https://doi.org/10.3390/rs14184430 - 06 Sep 2022
Cited by 1 | Viewed by 1654
Abstract
In recent years, the development of China’s marine aquaculture has brought serious challenges to the marine ecological environment. Therefore, it is significant to classify and extract the aquaculture zone and spatial distribution in order to provide a reference for aquaculture management. However, considering [...] Read more.
In recent years, the development of China’s marine aquaculture has brought serious challenges to the marine ecological environment. Therefore, it is significant to classify and extract the aquaculture zone and spatial distribution in order to provide a reference for aquaculture management. However, considering the complex marine aquaculture environment, it is difficult for traditional remote sensing technology and deep learning to achieve a breakthrough in the extraction of large-scale aquaculture zones so far. This study proposes a method based on the combination of piecewise linear stretching and R3Det to classify and extract raft aquaculture and cage aquaculture zones. The grayscale value is changed by piecewise linear stretching to reduce the influence of complex aquaculture backgrounds on the extraction accuracy, to effectively highlight the appearance characteristics of the aquaculture zone, and to improve the image contrast. On this basis, the aquaculture zone is classified and extracted by R3Det. Taking the aquaculture zone of Sansha Bay as the research object, the experimental results showed that the accuracy of R3Det in extracting the number of raft aquaculture and cage aquaculture zones was 98.91% and 97.21%, respectively, and the extraction precision of the area of the aquaculture zone reached 92.08%. The proposed method can classify and extract large-scale marine aquaculture zones more simply and efficiently than common remote sensing techniques. Full article
(This article belongs to the Special Issue Machine Learning and GeoAI for Remote Sensing Environmental Monitoring)
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