A comprehensive review of deep learning applications in hydrology and water resources
The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume,
variety and velocity of water-related data are increasing due to large-scale sensor networks …
variety and velocity of water-related data are increasing due to large-scale sensor networks …
Analysis on change detection techniques for remote sensing applications: A review
Satellite images taken on the earth's surface are analyzed to identify the spatial and
temporal changes that have occurred naturally or manmade. Real-time prediction of change …
temporal changes that have occurred naturally or manmade. Real-time prediction of change …
[HTML][HTML] Landslide detection in the Himalayas using machine learning algorithms and U-Net
Event-based landslide inventories are essential sources to broaden our understanding of
the causal relationship between triggering events and the occurring landslides. Moreover …
the causal relationship between triggering events and the occurring landslides. Moreover …
[HTML][HTML] Machine learning algorithms for urban land use planning: A review
V Chaturvedi, WT de Vries - Urban Science, 2021 - mdpi.com
Urbanization is persistent globally and has increasingly significant spatial and
environmental consequences. It is especially challenging in developing countries due to the …
environmental consequences. It is especially challenging in developing countries due to the …
Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution
Abstract Convolutional Neural Network (CNN) has been increasingly used for land cover
mapping of remotely sensed imagery. However, large-area classification using traditional …
mapping of remotely sensed imagery. However, large-area classification using traditional …
Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images
Deep learning convolutional neural network (CNN) is popular as being widely used for
classification of unstructured data. Land use land cover (LULC) classification using remote …
classification of unstructured data. Land use land cover (LULC) classification using remote …
Coupling of deep learning and remote sensing: a comprehensive systematic literature review
M Yasir, W Jianhua, L Shanwei, H Sheng… - … Journal of Remote …, 2023 - Taylor & Francis
This study is conducted in accordance with a systematic literature review (SLR) protocol.
SLR is tasked with finding publications, publishers, deep learning types, enhanced and …
SLR is tasked with finding publications, publishers, deep learning types, enhanced and …
[HTML][HTML] Forest damage assessment using deep learning on high resolution remote sensing data
ZM Hamdi, M Brandmeier, C Straub - Remote Sensing, 2019 - mdpi.com
Storms can cause significant damage to forest areas, affecting biodiversity and infrastructure
and leading to economic loss. Thus, rapid detection and mapping of windthrows are …
and leading to economic loss. Thus, rapid detection and mapping of windthrows are …
[HTML][HTML] Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach
Rainfall-induced landslide inventories can be compiled using remote sensing and
topographical data, gathered using either traditional or semi-automatic supervised methods …
topographical data, gathered using either traditional or semi-automatic supervised methods …
[HTML][HTML] Mapping urban tree cover changes using object-based convolution neural network (OB-CNN)
Urban trees provide social, economic, environmental and ecosystem services benefits that
improve the liveability of cities and contribute to individual and community wellbeing. There …
improve the liveability of cities and contribute to individual and community wellbeing. There …