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Land
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GeoAI for Land Use Observations, Analysis and Forecasting
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GeoAI for Land Use Observations, Analysis and Forecasting

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 11055

Special Issue Editors

Dr. Wenfeng Zheng

E-Mail Website
Guest Editor
Research Center for Machine Perception and Intelligent Systems, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: GIS/RS; AI/ML; geospatial AI; complex dynamics; geoinformatics; spatial computation and modeling of community resilience/sustainability; data science and statistics in land use; geo-simulation of human and environmental systems; GeoAI; integrated geo-cyber-infrastructures; urban planning; land development; land classification and observation; urbanization; space value modelling; social sensing; land management; land policy; wetland observation; classification and governance
Special Issues, Collections and Topics in MDPI journals
Dr. Kenan Li

E-Mail Website
Guest Editor
Department of Epidemiology and Biostatistics, College of Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA
Interests: geoinformatics; spatial computation and modeling of community resilience/sustainability; data science and statistics in land use; geo-simulation of human and environmental systems; GeoAI (artificial intelligence) frameworks; integrated geo-cyber-infrastructures; urban planning; GIS/RS; AI/ML; social equity; land development; urbanization; space value modelling; social sensing; GeoAI; land management; land policy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xuan Liu

E-Mail Website
Guest Editor
School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: geoInformatics; urban planning; urban renewal; real estate; GIS/RS; AI/ML; social equity; land development; urbanization; space value modelling; post-productivism transformation; social sensing; GeoAI; land management; land policy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

GeoAI, or geographic artificial intelligence, has emerged as a powerful tool for the observation, analysis, and forecasting of land use patterns that combines innovative artificial intelligence methods from space science, machine learning, deep learning, data mining, and high-performance computing to extract knowledge from spatial and temporal big data. GeoAI plays an important role in pushing geographic information science (GIS) and Earth observation toward a new stage of development by enhancing traditional geospatial analysis and mapping, as well as by changing the way we understand and manage complex human natural systems. When combined with remote sensing data, GeoAI can classify and map land cover, identify changes in land use, and predict future trends. In short, it has revolutionized the way we approach agricultural production, environmental protection, urban planning, and natural resource management.

Deep learning has transformed our understanding and utilization of both time and space. Through advanced neural network architectures, we can now extract meaningful patterns and representations from temporal and spatial data. By using remote sensing data and deep learning algorithms, it enables the classification and monitoring of land cover, aids in urban planning and resource management, supports decision-making in agriculture for increased productivity and food security, contributes to environmental protection and natural resource management, and provides decision support for sustainable land use policies.

Overall, GeoAI has the potential to foster far-reaching changes to our understanding of land use patterns and their impact on the environment and, as this technology continues to evolve, we can expect to see increasingly sophisticated applications. GeoAI has enormous potential to contribute to a more sustainable future.

In this Special Issue, we seek groundbreaking research and case studies that demonstrate future applications and advances in geographic artificial intelligence. Relevant topics include, but are not limited to the following:

  • Artificial intelligence for land use;
  • Geospatial artificial intelligence (geospatial AI or GeoAI) for land use;
  • AI in geostatistics and spatiotemporal simulation;
  • AI For geospatial data acquisition, analysis, planning, and prediction;
  • Hyperspectral imaging (HSI), lidar, and synthetic aperture radar (SAR);
  • Land change detection;
  • Natural resource management;
  • Semantic reasoning, semantic representation, and knowledge bases;
  • Object detection and semantic segmentation;
  • Natural disaster forecasting;
  • Contrastive learning, representation learning, and reinforcement learning;
  • Meta-learning, transfer learning, and few-shot learning;
  • Spatiotemporal integration;
  • Time series prediction and forecasting;
  • Multimodal artificial intelligence;
  • Visual- and spatial-based perception enhancement and reasoning;
  • Image denoising and high-resolution;
  • Visual question answer (VQA) and visual reasoning;
  • Large language model (LLM) and GenAI;
  • Wetland classification and forecasting

Dr. Wenfeng Zheng
Dr. Kenan Li
Prof. Dr. Xuan Liu
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. Land is an international peer-reviewed open access monthly 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 2600 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
  • land use and land cover
  • GeoAI
  • AI and machine learning

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  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (8 papers)

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Research

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23 pages, 8801 KiB  
Article
Intelligent Recommendation of Multi-Scale Response Strategies for Land Drought Events
by Lei He, Yuheng Lei, Yizhuo Yang, Bin Liu, Yuxia Li, Youcai Zhao and Dan Tang
Land 2025, 14(1), 42; https://doi.org/10.3390/land14010042 - 28 Dec 2024
Viewed by 316
Abstract
Currently, land drought events have become a frequent and serious global disaster. How to address these droughts has become a major issue for researchers. Traditional response strategies for land drought events have been determined by experts based on the severity levels of the [...] Read more.
Currently, land drought events have become a frequent and serious global disaster. How to address these droughts has become a major issue for researchers. Traditional response strategies for land drought events have been determined by experts based on the severity levels of the events. However, these methods do not account for temporal variations or the specific risks of different areas. As a result, they overlooked the importance of spatio-temporal multi-scale strategies. This research proposes a multi-scale response strategy recommendation model for land drought events. The model integrates characteristics of drought-causing factors, disaster-prone environments, and hazard-bearing bodies using case-based reasoning (CBR). Additionally, the analytic hierarchy process (AHP) and entropy weighting methods (EWMs) are introduced to assign weights to the feature attributes. A case retrieval algorithm is developed based on the similarity of these attributes and the structural similarities of drought cases. The research further classifies emergency strategies into long-term and short-term approaches. Each approach has a corresponding correction algorithm. For short-term strategies, a correction algorithm based on differential evolutions is applied. For long-term strategies, a correction algorithm based on drought risk assessment is developed. The algorithm considers factors such as drought risk, vulnerability, and exposure. It facilitates multi-scale decision-making for drought events. The candidate case obtained using the correction algorithm shows an overall attribute similarity of 94.7% with the real case. The emergency response levels match between the two cases. However, the funding required in the candidate case is CNY 327 million less than the actual expenditure. Full article
(This article belongs to the Special Issue GeoAI for Land Use Observations, Analysis and Forecasting)
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19 pages, 9557 KiB  
Article
CGBi_YOLO: Lightweight Land Target Detection Network
by Ruiyang Wang, Siyu Lu, Jiawei Tian, Lirong Yin, Lei Wang, Xiaobing Chen and Wenfeng Zheng
Land 2024, 13(12), 2060; https://doi.org/10.3390/land13122060 - 30 Nov 2024
Viewed by 610
Abstract
Object detection algorithms for optical remote sensing images often face challenges in computational efficiency, particularly when detecting small and densely packed targets. This paper introduces CGBi_YOLO, a novel lightweight land target detection network designed to optimize computational resource utilization while maintaining detection capabilities [...] Read more.
Object detection algorithms for optical remote sensing images often face challenges in computational efficiency, particularly when detecting small and densely packed targets. This paper introduces CGBi_YOLO, a novel lightweight land target detection network designed to optimize computational resource utilization while maintaining detection capabilities for small-scale targets. Our approach incorporates an innovative lightweight optimization strategy featuring a new lightweight backbone feature extraction network: CSPGhostNet. This model significantly enhances the detection ability of small objects within optical remote sensing images without increasing computational demands. The efficacy of the proposed model is validated through rigorous experimentation on the DOTA dataset. Compared to the baseline model, CGBi_YOLO achieves a 30% reduction in parameters and a 36% increase in inference speed. The model demonstrates exceptional performance in handling small and densely packed targets within optical remote sensing images, showcasing its potential for real-world applications in fields such as environmental monitoring, urban planning, and disaster management. Full article
(This article belongs to the Special Issue GeoAI for Land Use Observations, Analysis and Forecasting)
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23 pages, 6173 KiB  
Article
Scene Classification of Remote Sensing Image Based on Multi-Path Reconfigurable Neural Network
by Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin and Lei Wang
Land 2024, 13(10), 1718; https://doi.org/10.3390/land13101718 - 20 Oct 2024
Viewed by 753
Abstract
Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to [...] Read more.
Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to achieve the best performance in scene classification of multiple remote sensing land images. Therefore, to determine which model is the best for the current recognition classification tasks, it is often necessary to select and experiment with many different models. However, finding the optimal model is accompanied by an increase in trial-and-error costs and is a waste of researchers’ time, and it is often impossible to find the right model quickly. To address the issue of existing models being too large for easy selection, this paper proposes a multi-path reconfigurable network structure and takes the multi-path reconfigurable residual network (MR-ResNet) model as an example. The reconfigurable neural network model allows researchers to selectively choose the required modules and reassemble them to generate customized models by splitting the trained models and connecting them through modules with different properties. At the same time, by introducing the concept of a multi-path input network, the optimal path is selected by inputting different modules, which shortens the training time of the model and allows researchers to easily find the network model suitable for the current application scenario. A lot of training data, computational resources, and model parameter experience are saved. Three public datasets, NWPU-RESISC45, RSSCN7, and SIRI-WHU datasets, were used for the experiments. The experimental results demonstrate that the proposed model surpasses the classic residual network (ResNet) in terms of both parameters and performance. Full article
(This article belongs to the Special Issue GeoAI for Land Use Observations, Analysis and Forecasting)
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21 pages, 4545 KiB  
Article
SkipResNet: Crop and Weed Recognition Based on the Improved ResNet
by Wenyi Hu, Tian Chen, Chunjie Lan, Shan Liu and Lirong Yin
Land 2024, 13(10), 1585; https://doi.org/10.3390/land13101585 - 29 Sep 2024
Cited by 1 | Viewed by 771
Abstract
Weeds have a detrimental effect on crop yield. However, the prevailing chemical weed control methods cause pollution of the ecosystem and land. Therefore, it has become a trend to reduce dependence on herbicides; realize a sustainable, intelligent weed control method; and protect the [...] Read more.
Weeds have a detrimental effect on crop yield. However, the prevailing chemical weed control methods cause pollution of the ecosystem and land. Therefore, it has become a trend to reduce dependence on herbicides; realize a sustainable, intelligent weed control method; and protect the land. In order to realize intelligent weeding, efficient and accurate crop and weed recognition is necessary. Convolutional neural networks (CNNs) are widely applied for weed and crop recognition due to their high speed and efficiency. In this paper, a multi-path input skip-residual network (SkipResNet) was put forward to upgrade the classification function of weeds and crops. It improved the residual block in the ResNet model and combined three different path selection algorithms. Experiments showed that on the plant seedling dataset, our proposed network achieved an accuracy of 95.07%, which is 0.73%, 0.37%, and 4.75% better than that of ResNet18, VGG19, and MobileNetV2, respectively. The validation results on the weed–corn dataset also showed that the algorithm can provide more accurate identification of weeds and crops, thereby reducing land contamination during the weeding process. In addition, the algorithm is generalizable and can be used in image classification in agriculture and other fields. Full article
(This article belongs to the Special Issue GeoAI for Land Use Observations, Analysis and Forecasting)
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19 pages, 11877 KiB  
Article
A Robust Dual-Mode Machine Learning Framework for Classifying Deforestation Patterns in Amazon Native Lands
by Julia Rodrigues, Mauricio Araújo Dias, Rogério Negri, Sardar Muhammad Hussain and Wallace Casaca
Land 2024, 13(9), 1427; https://doi.org/10.3390/land13091427 - 4 Sep 2024
Viewed by 1013
Abstract
The integrated use of remote sensing and machine learning stands out as a powerful and well-established approach for dealing with various environmental monitoring tasks, including deforestation detection. In this paper, we present a tunable, data-driven methodology for assessing deforestation in the Amazon biome, [...] Read more.
The integrated use of remote sensing and machine learning stands out as a powerful and well-established approach for dealing with various environmental monitoring tasks, including deforestation detection. In this paper, we present a tunable, data-driven methodology for assessing deforestation in the Amazon biome, with a particular focus on protected conservation reserves. In contrast to most existing works from the specialized literature that typically target vast forest regions or privately used lands, our investigation concentrates on evaluating deforestation in particular, legally protected areas, including indigenous lands. By integrating the open data and resources available through the Google Earth Engine, our framework is designed to be adaptable, employing either anomaly detection methods or artificial neural networks for classifying deforestation patterns. A comprehensive analysis of the classifiers’ accuracy, generalization capabilities, and practical usage is provided, with a numerical assessment based on a case study in the Amazon rainforest regions of São Félix do Xingu and the Kayapó indigenous reserve. Full article
(This article belongs to the Special Issue GeoAI for Land Use Observations, Analysis and Forecasting)
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20 pages, 11083 KiB  
Article
Predicting Land Use and Land Cover Changes in the Chindwin River Watershed of Myanmar Using Multilayer Perceptron-Artificial Neural Networks
by Theint Thandar Bol and Timothy O. Randhir
Land 2024, 13(8), 1160; https://doi.org/10.3390/land13081160 - 29 Jul 2024
Viewed by 1216
Abstract
This study investigates the potential anthropogenic land use activities in the 114,000-km2 Chindwin River Watershed (CRW) in northwestern Myanmar, a biodiversity hotspot. This research evaluates current and future land use scenarios, particularly focusing on areas that provide ecosystem services for local communities [...] Read more.
This study investigates the potential anthropogenic land use activities in the 114,000-km2 Chindwin River Watershed (CRW) in northwestern Myanmar, a biodiversity hotspot. This research evaluates current and future land use scenarios, particularly focusing on areas that provide ecosystem services for local communities and those essential for biodiversity conservation. Remote sensing and geographical information systems were employed to evaluate land use changes in the CRW. We used a supervised classification approach with a random tree to generate land use and land cover (LULC) classifications. We calculated the percentage of change in LULC from 2010 to 2020 and projected future LULC change scenarios for approximately 2030 and 2050. The accuracy of the LULC maps was validated using Cohen’s Kappa statistics. The multilayer perceptron artificial neural network (MLP-ANN) algorithm was utilized to predict future LULC. Our study found that human settlements, wetlands, and bare land areas have increased while forest land has declined. The area covered by human settlements (0.36% of the total in 2000) is projected to increase from 264 km2 in 2000 to 424 km2 by 2050. The study also revealed that forest land has connections to other land categories, indicating a transformation of forest land into other types. The predicted future land use until 2050 reflects the potential impacts of urbanization, population growth, and infrastructure development in the CRW. Full article
(This article belongs to the Special Issue GeoAI for Land Use Observations, Analysis and Forecasting)
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32 pages, 17405 KiB  
Article
A Scientific Investigation of the Shangfang Mountain Yunshui Cave in Beijing Based on LiDAR Technology
by Xinyue Liu, Yanhui Shan, Gang Ai, Zhengfeng Du, Anran Shen and Ningfei Lei
Land 2024, 13(6), 895; https://doi.org/10.3390/land13060895 - 20 Jun 2024
Viewed by 1146
Abstract
The Yunshui Cave in Shangfang Mountain, Beijing, is a famous high-altitude karst cave in northern China. As the third scientific survey of Yunshui Cave in history, this is the first time to use the latest LiDAR technology to carry out a related detection [...] Read more.
The Yunshui Cave in Shangfang Mountain, Beijing, is a famous high-altitude karst cave in northern China. As the third scientific survey of Yunshui Cave in history, this is the first time to use the latest LiDAR technology to carry out a related detection survey. Traditional cave measurement methods are limited by natural conditions and make it difficult to reach the destination. Traditional methods mainly rely on experience and obtain data with strong subjectivity, making it difficult to conduct quantitative research and obtain reproducible results in the current information era. Applying LiDAR technology to cave measurement can obtain comprehensive and accurate digital measurement results within the same survey time and reveal many richer and more accurate features of Yunshui Cave. The obtained digital measurement results can be used for 3D modeling as well as provide a large amount of accurate basic data and preliminary materials for subsequent geological, environmental, and archaeological investigation and analysis, as well as cultural and tourism resource development. The rapid geological survey of Shangfang Mountain Yunshui Cave using LiDAR technology shows that LiDAR cave geological survey technology can achieve real-time collection of centimeter-level accuracy and generate billions of points of cloud data, greatly improving survey efficiency and accuracy. At the same time, digital survey results can be obtained. Through modeling and GIS technology, all on-site survey details can be easily moved back to the laboratory for real-scene reproduction, network sharing, and dissemination. This study provides a foundation for future explorations of the Yunshui cave and highlights the potential for LiDAR techniques to enhance our understanding of complex geological structures such as caves. Full article
(This article belongs to the Special Issue GeoAI for Land Use Observations, Analysis and Forecasting)
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Review

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20 pages, 3329 KiB  
Review
Fire Detection with Deep Learning: A Comprehensive Review
by Rodrigo N. Vasconcelos, Washington J. S. Franca Rocha, Diego P. Costa, Soltan G. Duverger, Mariana M. M. de Santana, Elaine C. B. Cambui, Jefferson Ferreira-Ferreira, Mariana Oliveira, Leonardo da Silva Barbosa and Carlos Leandro Cordeiro
Land 2024, 13(10), 1696; https://doi.org/10.3390/land13101696 - 17 Oct 2024
Cited by 1 | Viewed by 3882
Abstract
Wildfires are a critical driver of landscape transformation on Earth, representing a dynamic and ephemeral process that poses challenges for accurate early detection. To address this challenge, researchers have increasingly turned to deep learning techniques, which have demonstrated remarkable potential in enhancing the [...] Read more.
Wildfires are a critical driver of landscape transformation on Earth, representing a dynamic and ephemeral process that poses challenges for accurate early detection. To address this challenge, researchers have increasingly turned to deep learning techniques, which have demonstrated remarkable potential in enhancing the performance of wildfire detection systems. This paper provides a comprehensive review of fire detection using deep learning, spanning from 1990 to 2023. This study employed a comprehensive approach, combining bibliometric analysis, qualitative and quantitative methods, and systematic review techniques to examine the advancements in fire detection using deep learning in remote sensing. It unveils key trends in publication patterns, author collaborations, and thematic focuses, emphasizing the remarkable growth in fire detection using deep learning in remote sensing (FDDL) research, especially from the 2010s onward, fueled by advancements in computational power and remote sensing technologies. The review identifies “Remote Sensing” as the primary platform for FDDL research dissemination and highlights the field’s collaborative nature, with an average of 5.02 authors per paper. The co-occurrence network analysis reveals diverse research themes, spanning technical approaches and practical applications, with significant contributions from China, the United States, South Korea, Brazil, and Australia. Highly cited papers are explored, revealing their substantial influence on the field’s research focus. The analysis underscores the practical implications of integrating high-quality input data and advanced deep-learning techniques with remote sensing for effective fire detection. It provides actionable recommendations for future research, emphasizing interdisciplinary and international collaboration to propel FDDL technologies and applications. The study’s conclusions highlight the growing significance of FDDL technologies and the necessity for ongoing advancements in computational and remote sensing methodologies. The practical takeaway is clear: future research should prioritize enhancing the synergy between deep learning techniques and remote sensing technologies to develop more efficient and accurate fire detection systems, ultimately fostering groundbreaking innovations. Full article
(This article belongs to the Special Issue GeoAI for Land Use Observations, Analysis and Forecasting)
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