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Advances and Challenges in Ultra-High-Resolution Land Cover and Land Use Classification

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

Deadline for manuscript submissions: 30 December 2024 | Viewed by 3193

Special Issue Editors


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Guest Editor
Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: forest; GIS; Copernicus; remote sensing; machine learning; land cover; vegetation mapping
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: geomatics; GNSS; mapping; earth observations; remote sensing; geographic information system; spatial analysis; image analysis; UAV; artificial intelligence; low-cost sensors; sensors integrations; data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land cover and land use (LCLU) classification has witnessed significant advancements in recent decades, propelled by the widespread availability of high-resolution optical imagery and from satellite and unmanned aerial vehicles (UAVs). Land cover classification at spatial resolutions finer than 10 cm is commonly referred to as ultra-high-resolution (UHR), distinguishing it from very-high-resolution (VHR) classifications, which denote resolutions less than 1 m. The utilization of specific classification algorithms, diverse processing platforms, and increasingly powerful computational resources has further enhanced the capabilities of LCLU classification. Integrating very-high-resolution imagery has notably transformed LCLU analysis, presenting new opportunities and challenges.

In particular, challenges arise from significant inter-class variability, where pixels with markedly different digital numbers belong to the same class and spectrally similar but semantically distant classes. Various techniques have been employed to address these complexities, including object-based image analysis (OBIA) and incorporating texture-related information or object relationships with surrounding elements (spatial relations).

This Special Issue of Remote Sensing aims to compile contributions focused on generating land cover and land use maps using ultra-high-resolution and very-high-resolution optical data derived from both UAVs and satellites, along with their integration, including model transfer and model upscaling/downscaling. Submissions related to diverse geographic areas, specific semantic classifications, methodologies for minimizing errors associated with UHR and VHR resolution, and applications in complex scenarios are encouraged.

Dr. Elena Belcore
Prof. Dr. Marco Piras
Guest Editors

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Keywords

  • land cover
  • land use
  • ultra-high-resolution (UHR)
  • very-high-resolution (VHR)
  • classification algorithms
  • OBIA
  • texture analysis
  • spatial components
  • UAV
  • satellite satellite–UAV data fusion
  • model upscaling
  • model downscaling

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Published Papers (3 papers)

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Research

37 pages, 92018 KiB  
Article
Semantic Mapping of Landscape Morphologies: Tuning ML/DL Classification Approaches for Airborne LiDAR Data
by Marco Cappellazzo, Giacomo Patrucco, Giulia Sammartano, Marco Baldo and Antonia Spanò
Remote Sens. 2024, 16(19), 3572; https://doi.org/10.3390/rs16193572 - 25 Sep 2024
Cited by 1 | Viewed by 891
Abstract
The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins [...] Read more.
The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated. Full article
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21 pages, 6250 KiB  
Article
Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery
by Edyta Puniach, Wojciech Gruszczyński, Paweł Ćwiąkała, Katarzyna Strząbała and Elżbieta Pastucha
Remote Sens. 2024, 16(18), 3444; https://doi.org/10.3390/rs16183444 - 17 Sep 2024
Viewed by 656
Abstract
This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery. The tested solutions included numerous vegetation indices (VIs) thresholding and neural networks (NNs). The analysis was conducted for two study areas for which surveys [...] Read more.
This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery. The tested solutions included numerous vegetation indices (VIs) thresholding and neural networks (NNs). The analysis was conducted for two study areas for which surveys were carried out using different UAVs and cameras. The ground sampling distances for the study areas were 10 mm and 15 mm, respectively. Reference classification was performed manually, obtaining approximately 24 million classified pixels for the first area and approximately 3.8 million for the second. This research study included an analysis of the impact of the season on the threshold values for the tested VIs and the impact of image patch size provided as inputs for the NNs on classification accuracy. The results of the conducted research study indicate a higher classification accuracy using NNs (about 96%) compared with the best of the tested VIs, i.e., Excess Blue (about 87%). Due to the highly imbalanced nature of the used datasets (non-urbanized areas constitute approximately 87% of the total datasets), the Matthews correlation coefficient was also used to assess the correctness of the classification. The analysis based on statistical measures was supplemented with a qualitative assessment of the classification results, which allowed the identification of the most important sources of differences in classification between VIs thresholding and NNs. Full article
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18 pages, 6668 KiB  
Article
Land-Use Composition, Distribution Patterns, and Influencing Factors of Villages in the Hehuang Valley, Qinghai, China, Based on UAV Photogrammetry
by Xiaoyu Li and Zhongbao Xin
Remote Sens. 2024, 16(12), 2213; https://doi.org/10.3390/rs16122213 - 18 Jun 2024
Viewed by 1126
Abstract
Rapid changes in land use have rendered existing data for land-use classification insufficient to meet the current data requirements for rural revitalization and improvements in the living environment. Therefore, we used unmanned aerial vehicle (UAV) remote sensing imagery and an object-based human-assisted approach [...] Read more.
Rapid changes in land use have rendered existing data for land-use classification insufficient to meet the current data requirements for rural revitalization and improvements in the living environment. Therefore, we used unmanned aerial vehicle (UAV) remote sensing imagery and an object-based human-assisted approach to obtain ultra-high-resolution land-use data for 55 villages and accurately analyzed village land-use composition and distribution patterns. The highest proportion of land use in the villages is built-up land (33.01% ± 8.89%), and the proportion of road land is 17.76% ± 6.92%. The proportions for forest land and grassland are 16.41% ± 7.80% and 6.51% ± 4.93%, respectively. The average size of the villages is 25.85 ± 17.93 hm2, which is below the national average. The villages have a relatively scattered distribution, mostly concentrated on both sides of the main roads. The correlation analysis indicates that mean annual temperature (MAT) and annual precipitation (AP) are the primary factors influencing the land-use composition of villages, with contribution rates of 50.56% and 12.51%, respectively. The use of UAV remote sensing imagery to acquire ultra-high-resolution land-use data will provide a scientific basis for the planning of the living environment in the villages of the Hehuang Valley. Full article
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