Ditapis dengan
Contribution of multispectral (optical and radar) satellite images to the cla…
The monitoring of different crops (cultivated plots) and types of surface (bare soils, etc.) is a crucial economic and environmental issue for the management of resources and human activity. In this context, the objective of this study is to evaluate the contribution of multispectral satellite imagery (optical and radar) to land use and land cover classification. Object-oriented supervised cla…
- Edisi
- Vol.84, February 2020
- ISBN/ISSN
- 1569-8432
- Deskripsi Fisik
- 13 hlm PDF, 5.659 KB
- Judul Seri
- International Journal of Applied Earth Observation and Geoinformation - Open Access
- No. Panggil
- 910.285
Accuracy assessment of NLCD 2011 percent impervious cover for selected USA me…
The emergence of high-resolution land cover data has created the opportunity to assess the accuracy of impervious cover (IC) provided by the National Land Cover Database (NLCD). We assessed the accuracy of the 900 m2 NLCD2011 %IC for 18 metropolitan areas throughout the conterminous United States using reference data from 1 m2 land cover data developed as part of the United States Environme…
- Edisi
- Vol.84, February 2020
- ISBN/ISSN
- 1569-8432
- Deskripsi Fisik
- 9 hlm PDF, 1.389 KB
- Judul Seri
- International Journal of Applied Earth Observation and Geoinformation - Open Access
- No. Panggil
- 910.285
Determination of future land use changes using remote sensing imagery and art…
Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource extraction, as well as natural phenomena, for example, erosion and climate change. LULC changes significantly impact ecosystem services, biodiversity, and human welfare. In this study, LULC changes i…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 6.696 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Capsule network-based approach for estimating grassland coverage using time s…
The degradation and desertification of grasslands pose a daunting challenge to China's arid and semiarid areas owing to the increasing demand for them in light of the rise of animal husbandry. Monitoring grasslands by using big data has emerged as a popular area of research in recent years. As grassland degradation is a slow and gradual process, the accurate identification of grassland cover is…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 3,657 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Semantic segmentation framework for atoll satellite imagery: An in-depth expl…
This paper presents a framework for semantic segmentation of satellite imagery aimed at studying atoll morphometrics. Recent advances in deep neural networks for automated segmentation have been valuable across a variety of satellite and aerial imagery applications, such as land cover classification, mineral characterization, and disaster impact assessment. However, identifying an appropriate s…
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 16 hlm PDF, 3.046 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Land use and land cover classification for change detection studies using con…
Efficient land use land cover (LULC) classification is crucial for environmental monitoring, urban planning, and resource management. This study investigates LULC changes in Nanjangud taluk, Mysuru district, Karnataka, India, using remote sensing (RS) and geographic information systems (GIS). This paper mainly focuses on the classification and change detection analysis of LULC in 2010 and 2020 …
- Edisi
- Vol.25, February 2025
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 17 hlm PDF, 11.125 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Comparative performance analysis of simple U-Net, residual attention U-Net, a…
Inland water bodies play a vital role at all scales in the terrestrial water balance and Earth’s climate variability. Thus, an inventory of inland waters is crucially important for hydrologic and ecological studies and management. Therefore, the main aim of this study was to develop a deep learning-based method for inventorying and mapping inland water bodies using the RGB band of high-resolu…
- Edisi
- Vol.21, March 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 11 hlm PDF, 3.036 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
End-to-end simulations to optimize imaging spectroscopy mission requirements …
CNES is currently carrying out a Phase A study to assess the feasibility of a future hyperspectral imaging sensor (10 m spatial resolution) combined with a panchromatic camera (2.5 m spatial resolution). This mission focuses on both high spatial and spectral resolution requirements, as inherited from previous French studies such as HYPEX, HYPXIM, and BIODIVERSITY. To meet user requirements, cos…
- Edisi
- Vol.12, April 2024
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 20 hlm PDF, 15.892 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data lan…
This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerou…
- Edisi
- Vol.10, December 2023
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 13 hlm PDF, 19,559 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Automatic labelling for semantic segmentation of VHR satellite images: Applic…
The application of deep learning methods to remote sensing data has produced good results in recent studies. A promising application area is automatic land cover classification (semantic segmentation) from very high-resolution satellite imagery. However, the deep learning methods require large, labelled training datasets that are suitable for the study area. Map data can be used as training dat…
- Edisi
- Vol.9, August 2023
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 11 hlm PDF, 15.060 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
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