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Application of Artificial Intelligence in Land Use and Land Cover Mapping

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

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 48116

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Special Issue Editors

Dr. Sawaid Abbas

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Guest Editor

1. Department of the Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong, China
2. Remote Sensing, GIS and Climatic Research Lab (RSGCRL), National Center of GIS and Space Applications, University of the Punjab, Lahore, Pakistan
Interests: remote sensing; artificial intelligence; cloud computing; big data; pattern recognition; sustainable development goals; landcover dynamics; urban ecology; tropical forest recovery; land degradation
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Prof. Dr. Janet E. Nichol

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Department of Geography, School of Global Studies, University of Sussex, Brighton, UK
Interests: geo-informatics; environmental change; landscape change; urban climate; air pollution; climate change; tropical forest ecology; aerosols
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Dr. Faisal M. Qamer

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Guest Editor

International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal
Interests: remote sensing; land degradation; croplands; droughts; environmental monitoring; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jianchu Xu

E-Mail Website
Guest Editor

Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China
Interests: land cover land use change; landscape restoration; biodiversity conservation
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Special Issue Information

Dear Colleagues,

Land cover monitoring provides critical insight to monitor and achieve Sustainable Development Goals (SDGs). During the last four decades, remote sensing has transformed from observation and monitoring to advanced understanding, for managing the planet’s resources. Land-use change data from satellite remote sensing, along with climate modelling and socio-economic indicators are playing vital roles in advancing interdisciplinary research.

The huge amount of data currently produced by modern Earth Observation (EO) satellite missions and Unmanned Aerial Vehicles (UAVs), availability of high-performance computing platforms and the development of artificial intelligence (AI) provide new opportunities to advance our knowledge about patterns of resource distribution and resource use. Machine learning approaches tailored for Earth Observation data can effectively support the challenges of spatial and temporal domain adaptation, hyperspectral data mining, integration of multi-source information and large-volume data analysis.

Considering these advances, this Special Issue invites manuscripts that present new developments and methodologies, best practices, and applications related to land use, land cover mapping and modelling through the implementation of artificial intelligence (fuzzy logic, neural networks, machine learning, deep learning, evolutionary computation, etc). We welcome submissions that provide the community with the most recent advancements on all aspects mentioned above. We welcome, Original Research Articles, Reviews, Letters, Technical Notes, as well as Highlight articles for a broader audience.

Dr. Sawaid Abbas
Prof. Janet E. Nichol
Dr. Faisal M. Qamer
Prof. Jianchu Xu
Guest Editors

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Keywords

Land Cover Land Use
Monitoring Change
Artificial Intelligence
Machine Learning and Deep Learning
Pattern Recognition and Data Mining
Hyper Temporal Mapping
High-Resolution Urban Landscape Mapping
Hyperspectral Remote Sensing
Biophysical and Social Data Integration
Sustainable Development Goals (SDGs)

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Published Papers (11 papers)

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17 pages, 11877 KiB

Open AccessArticle

SRT: A Spectral Reconstruction Network for GF-1 PMS Data Based on Transformer and ResNet

by Kai Mu, Ziyuan Zhang, Yurong Qian, Suhong Liu, Mengting Sun and Ranran Qi

Remote Sens. 2022, 14(13), 3163; https://doi.org/10.3390/rs14133163 - 1 Jul 2022

Cited by 6 | Viewed by 2535

Abstract

The time of acquiring remote sensing data was halved after the joint operation of Gao Fen-6 (GF-6) and Gao Fen-1 (GF-1) satellites. Meanwhile, GF-6 added four bands, including the “red-edge” band that can effectively reflect the unique spectral characteristics of crops. However, GF-1 data do not contain these bands, which greatly limits their application to crop-related joint monitoring. In this paper, we propose a spectral reconstruction network (SRT) based on Transformer and ResNet to reconstruct the missing bands of GF-1. SRT is composed of three modules: (1) The transformer feature extraction module (TFEM) fully extracts the correlation features between spectra. (2) The residual dense module (RDM) reconstructs local features and avoids the vanishing gradient problem. (3) The residual global construction module (RGM) reconstructs global features and preserves texture details. Compared with competing methods, such as AWAN, HRNet, HSCNN-D, and M2HNet, the proposed method proved to have higher accuracy by a margin of the mean relative absolute error (MRAE) and root mean squared error (RMSE) of 0.022 and 0.009, respectively. It also achieved the best accuracy in supervised classification based on support vector machine (SVM) and spectral angle mapper (SAM). Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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19 pages, 4545 KiB

Open AccessArticle

Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning

by Davide Lomeo and Minerva Singh

Remote Sens. 2022, 14(10), 2291; https://doi.org/10.3390/rs14102291 - 10 May 2022

Cited by 10 | Viewed by 4149

Abstract

This paper proposes a cloud-based mangrove monitoring framework that uses Google Collaboratory and Google Earth Engine to classify mangroves in Southeast Asia (SEA) using satellite remote sensing imagery (SRSI). Three multi-class classification convolutional neural network (CNN) models were generated, showing F1-score values as high as 0.9 in only six epochs of training. Mangrove forests are tropical and subtropical environments that provide essential ecosystem services to local biota and coastal communities and are considered the most efficient vegetative carbon stock globally. Despite their importance, mangrove forest cover continues to decline worldwide, especially in SEA. Scientists have produced monitoring tools based on SRSI and CNNs to identify deforestation hotspots and drive targeted interventions. Nevertheless, although CNNs excel in distinguishing between different landcover types, their greatest limitation remains the need for significant computing power to operate. This may not always be feasible, especially in developing countries. The proposed framework is believed to provide a robust, low-cost, cloud-based, near-real-time monitoring tool that could serve governments, environmental agencies, and researchers, to help map mangroves in SEA. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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13 pages, 9981 KiB

Open AccessArticle

Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing

by Fengnong Chen, Yao Zhang, Jingcheng Zhang, Lianmeng Liu and Kaihua Wu

Remote Sens. 2022, 14(4), 945; https://doi.org/10.3390/rs14040945 - 15 Feb 2022

Cited by 11 | Viewed by 3440

Abstract

Rice false smut is known as the cancer of rice. The disease is becoming increasingly prominent and is one of the major diseases in rice. However, prevention and treatment of this disease relies on “Centralized pesticide spraying”. However, indiscriminate spraying leads to more pesticide residue, and impacts ecological and food safety. To obtain more objective results, different experimental planting forms are necessary. This study collected data at a complex planting environment based on “near earth remote sensing” using a frame-based hyperspectral device. We used mixed detection methods to differentiate between healthy rice and U. virens infected rice. There were 49 arrangements and more than 196 differentiation models between healthy and diseased rice, including 7 sowing data plots, 2 farm management types, and 23 pattern recognition methods. Finally, the real accuracy was mostly above 95%. In particular, with the increase of epoch and iteration, feature sequences based on deep learning could achieve better results; most of the accuracies were 100% with 100 epochs. We also found that differentiation accuracy was not necessarily correlated with the sowing dates and farm management. Finally, the detection method was verified according to the actual investigation results in the field. The prescription map of disease incidence was generated, which provided a theoretical basis for the follow-up precision plant protection work. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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18 pages, 10616 KiB

Open AccessArticle

Integrating Phenological and Geographical Information with Artificial Intelligence Algorithm to Map Rubber Plantations in Xishuangbanna

by Jianbo Yang, Jianchu Xu and De-Li Zhai

Remote Sens. 2021, 13(14), 2793; https://doi.org/10.3390/rs13142793 - 16 Jul 2021

Cited by 18 | Viewed by 3470

Abstract

Most natural rubber trees (Hevea brasiliensis) are grown on plantations, making rubber an important industrial crop. Rubber plantations are also an important source of household income for over 20 million people. The accurate mapping of rubber plantations is important for both local governments and the global market. Remote sensing has been a widely used approach for mapping rubber plantations, typically using optical remote sensing data obtained at the regional scale. Improving the efficiency and accuracy of rubber plantation maps has become a research hotspot in rubber-related literature. To improve the classification efficiency, researchers have combined the phenology, geography, and texture of rubber trees with spectral information. Among these, there are three main classifiers: maximum likelihood, QUEST decision tree, and random forest methods. However, until now, no comparative studies have been conducted for the above three classifiers. Therefore, in this study, we evaluated the mapping accuracy based on these three classifiers, using four kinds of data input: Landsat spectral information, phenology–Landsat spectral information, topography–Landsat spectral information, and phenology–topography–Landsat spectral information. We found that the random forest method had the highest mapping accuracy when compared with the maximum likelihood and QUEST decision tree methods. We also found that adding either phenology or topography could improve the mapping accuracy for rubber plantations. When either phenology or topography were added as parameters within the random forest method, the kappa coefficient increased by 5.5% and 6.2%, respectively, compared to the kappa coefficient for the baseline Landsat spectral band data input. The highest accuracy was obtained from the addition of both phenology–topography–Landsat spectral bands to the random forest method, achieving a kappa coefficient of 97%. We therefore mapped rubber plantations in Xishuangbanna using the random forest method, with the addition of phenology and topography information from 1990–2020. Our results demonstrated the usefulness of integrating phenology and topography for mapping rubber plantations. The machine learning approach showed great potential for accurate regional mapping, particularly by incorporating plant habitat and ecological information. We found that during 1990–2020, the total area of rubber plantations had expanded to over three times their former area, while natural forests had lost 17.2% of their former area. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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17 pages, 14761 KiB

Open AccessArticle

Land Use and Land Cover Mapping Using RapidEye Imagery Based on a Novel Band Attention Deep Learning Method in the Three Gorges Reservoir Area

by Xin Zhang, Ling Du, Shen Tan, Fangming Wu, Liang Zhu, Yuan Zeng and Bingfang Wu

Remote Sens. 2021, 13(6), 1225; https://doi.org/10.3390/rs13061225 - 23 Mar 2021

Cited by 12 | Viewed by 3935

Abstract

Land use/land cover (LULC) change has been recognized as one of the most important indicators to study ecological and environmental changes. Remote sensing provides an effective way to map and monitor LULC change in real time and for large areas. However, with the increasing spatial resolution of remote sensing imagery, traditional classification approaches cannot fully represent the spectral and spatial information from objects and thus have limitations in classification results, such as the “salt and pepper” effect. Nowadays, the deep semantic segmentation methods have shown great potential to solve this challenge. In this study, we developed an adaptive band attention (BA) deep learning model based on U-Net to classify the LULC in the Three Gorges Reservoir Area (TGRA) combining RapidEye imagery and topographic information. The BA module adaptively weighted input bands in convolution layers to address the different importance of the bands. By comparing the performance of our model with two typical traditional pixel-based methods including classification and regression tree (CART) and random forest (RF), we found a higher overall accuracy (OA) and a higher Intersection over Union (IoU) for all classification categories using our model. The OA and mean IoU of our model were 0.77 and 0.60, respectively, with the BA module and were 0.75 and 0.58, respectively, without the BA module. The OA and mean IoU of CART and RF were both below 0.51 and 0.30, respectively, although RF slightly outperformed CART. Our model also showed a reasonable classification accuracy in independent areas well outside the training area, which indicates the strong model generalizability in the spatial domain. This study demonstrates the novelty of our proposed model for large-scale LULC mapping using high-resolution remote sensing data, which well overcomes the limitations of traditional classification approaches and suggests the consideration of band weighting in convolution layers. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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11 pages, 7390 KiB

Open AccessCommunication

A Novel Framework Based on Mask R-CNN and Histogram Thresholding for Scalable Segmentation of New and Old Rural Buildings

by Ying Li, Weipan Xu, Haohui Chen, Junhao Jiang and Xun Li

Remote Sens. 2021, 13(6), 1070; https://doi.org/10.3390/rs13061070 - 11 Mar 2021

Cited by 47 | Viewed by 4363

Abstract

Mapping new and old buildings are of great significance for understanding socio-economic development in rural areas. In recent years, deep neural networks have achieved remarkable building segmentation results in high-resolution remote sensing images. However, the scarce training data and the varying geographical environments have posed challenges for scalable building segmentation. This study proposes a novel framework based on Mask R-CNN, named Histogram Thresholding Mask Region-Based Convolutional Neural Network (HTMask R-CNN), to extract new and old rural buildings even when the label is scarce. The framework adopts the result of single-object instance segmentation from the orthodox Mask R-CNN. Further, it classifies the rural buildings into new and old ones based on a dynamic grayscale threshold inferred from the result of a two-object instance segmentation task where training data is scarce. We found that the framework can extract more buildings and achieve a much higher mean Average Precision (mAP) than the orthodox Mask R-CNN model. We tested the novel framework’s performance with increasing training data and found that it converged even when the training samples were limited. This framework’s main contribution is to allow scalable segmentation by using significantly fewer training samples than traditional machine learning practices. That makes mapping China’s new and old rural buildings viable. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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20 pages, 10328 KiB

Open AccessArticle

Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

by Han Gao, Jinhui Guo, Peng Guo and Xiuwan Chen

Remote Sens. 2021, 13(3), 364; https://doi.org/10.3390/rs13030364 - 21 Jan 2021

Cited by 12 | Viewed by 4018

Abstract

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha). Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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20 pages, 16454 KiB

Open AccessArticle

Development of Land Cover Classification Model Using AI Based FusionNet Network

by Jinseok Park, Seongju Jang, Rokgi Hong, Kyo Suh and Inhong Song

Remote Sens. 2020, 12(19), 3171; https://doi.org/10.3390/rs12193171 - 27 Sep 2020

Cited by 4 | Viewed by 4671

Abstract

Prompt updates of land cover maps are important, as spatial information of land cover is widely used in many areas. However, current manual digitizing methods are time consuming and labor intensive, hindering rapid updates of land cover maps. The objective of this study was to develop an artificial intelligence (AI) based land cover classification model that allows for rapid land cover classification from high-resolution remote sensing (HRRS) images. The model comprises of three modules: pre-processing, land cover classification, and post-processing modules. The pre-processing module separates the HRRS image into multiple aspects by overlapping 75% using the sliding window algorithm. The land cover classification module was developed using the convolutional neural network (CNN) concept, based the FusionNet network and used to assign a land cover type to the separated HRRS images. Post-processing module determines ultimate land cover types by summing up the separated land cover result from the land cover classification module. Model training and validation were conducted to evaluate the performance of the developed model. The land cover maps and orthographic images of 547.29 km² in area from the Jeonnam province in Korea were used to train the model. For model validation, two spatial and temporal different sites, one from Subuk-myeon of Jeonnam province in 2018 and the other from Daseo-myeon of Chungbuk province in 2016, were randomly chosen. The model performed reasonably well, demonstrating overall accuracies of 0.81 and 0.71, and kappa coefficients of 0.75 and 0.64, for the respective validation sites. The model performance was better when only considering the agricultural area by showing overall accuracy of 0.83 and kappa coefficients of 0.73. It was concluded that the developed model may assist rapid land cover update especially for agricultural areas and incorporation field boundary lineation is suggested as future study to further improve the model accuracy. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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23 pages, 7156 KiB

Open AccessArticle

A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images

by Nian Shi, Keming Chen, Guangyao Zhou and Xian Sun

Remote Sens. 2020, 12(18), 3057; https://doi.org/10.3390/rs12183057 - 18 Sep 2020

Cited by 11 | Viewed by 3225

Abstract

With the development of remote sensing technologies, change detection in heterogeneous images becomes much more necessary and significant. The main difficulty lies in how to make input heterogeneous images comparable so that the changes can be detected. In this paper, we propose an end-to-end heterogeneous change detection method based on the feature space constraint. First, considering that the input heterogeneous images are in two distinct feature spaces, two encoders with the same structure are used to extract features, respectively. A decoder is used to obtain the change map from the extracted features. Then, the Gram matrices, which include the correlations between features, are calculated to represent different feature spaces, respectively. The squared Euclidean distance between Gram matrices, termed as feature space loss, is used to constrain the extracted features. After that, a combined loss function consisting of the binary cross entropy loss and feature space loss is designed for training the model. Finally, the change detection results between heterogeneous images can be obtained when the model is trained well. The proposed method can constrain the features of two heterogeneous images to the same feature space while keeping their unique features so that the comparability between features can be enhanced and better detection results can be achieved. Experiments on two heterogeneous image datasets consisting of optical and SAR images demonstrate the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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16 pages, 4149 KiB

Open AccessTechnical Note

Extracting the Tailings Ponds from High Spatial Resolution Remote Sensing Images by Integrating a Deep Learning-Based Model

by Jianjun Lyu, Ying Hu, Shuliang Ren, Yao Yao, Dan Ding, Qingfeng Guan and Liufeng Tao

Remote Sens. 2021, 13(4), 743; https://doi.org/10.3390/rs13040743 - 17 Feb 2021

Cited by 22 | Viewed by 4526

Abstract

Due to a lack of data and practical models, few studies have extracted tailings pond margins in large areas. In addition, there is no public dataset of tailings ponds available for relevant research. This study proposed a new deep learning-based framework for extracting tailings pond margins from high spatial resolution (HSR) remote sensing images by combining You Only Look Once (YOLO) v4 and the random forest algorithm. At the same time, we created an open source tailings pond dataset based on HSR remote sensing images. Taking Tongling city as the study area, the proposed model can detect tailings pond locations with high accuracy and efficiency from a large HSR remote sensing image (precision = 99.6%, recall = 89.9%, mean average precision = 89.7%). An optimal random forest model and morphological processing were utilized to further extract accurate tailings pond margins from the target areas. The final map of the entire study area was obtained with high accuracy. Compared with the random forest algorithm, the total extraction time was reduced by nearly 99%. This study can be beneficial to mine monitoring and ecological environmental governance. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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14 pages, 1383 KiB

Open AccessLetter

Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation

by Carlos García Rodríguez, Jordi Vitrià and Oscar Mora

Remote Sens. 2020, 12(22), 3836; https://doi.org/10.3390/rs12223836 - 23 Nov 2020

Cited by 11 | Viewed by 3114

Abstract

In recent years, different deep learning techniques were applied to segment aerial and satellite images. Nevertheless, state of the art techniques for land cover segmentation does not provide accurate results to be used in real applications. This is a problem faced by institutions and companies that want to replace time-consuming and exhausting human work with AI technology. In this work, we propose a method that combines deep learning with a human-in-the-loop strategy to achieve expert-level results at a low cost. We use a neural network to segment the images. In parallel, another network is used to measure uncertainty for predicted pixels. Finally, we combine these neural networks with a human-in-the-loop approach to produce correct predictions as if developed by human photointerpreters. Applying this methodology shows that we can increase the accuracy of land cover segmentation tasks while decreasing human intervention. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Land Use and Land Cover Mapping)

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