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[HTML][HTML] Faster and lightweight: an improved YOLOv5 object detector for remote sensing images
In recent years, the realm of deep learning has witnessed significant advancements,
particularly in object detection algorithms. However, the unique challenges posed by remote …
particularly in object detection algorithms. However, the unique challenges posed by remote …
Map** and spatiotemporal dynamics of land-use and land-cover change based on the Google Earth Engine cloud platform from Landsat imagery: A case study of …
C Chen, X Yang, S Jiang, Z Liu - Heliyon, 2023 - cell.com
Land resources are an essential foundation for socioeconomic development. Island land
resources are limited, the type changes are particularly frequent, and the environment is …
resources are limited, the type changes are particularly frequent, and the environment is …
[HTML][HTML] A trained Mask R-CNN model over PlanetScope imagery for very-high resolution surface water map** in boreal forest-tundra
Abstract Small water bodies (< 0.01 km 2) showing diverse limnological properties occur in
great abundance across the boreal forest and tundra landscapes of the Arctic and Subarctic …
great abundance across the boreal forest and tundra landscapes of the Arctic and Subarctic …
The dynamic-static dual-branch deep neural network for urban speeding hotspot identification using street view image data
The visual information regarding the road environment can influence drivers' perception and
judgment, often resulting in frequent speeding incidents. Identifying speeding hotspots in …
judgment, often resulting in frequent speeding incidents. Identifying speeding hotspots in …
Understanding the potentials of early-season crop type map** by using Landsat-8, Sentinel-1/2, and GF-1/6 data
C Wang, X Zhang, W Wang, H Wei, J Wang, Z Li… - … and Electronics in …, 2024 - Elsevier
Early-season crop identification by remote sensing is challenging due to insufficient spectral
and temporal information at early stage, especially when relying on single satellite data …
and temporal information at early stage, especially when relying on single satellite data …
Satellite image classification using convolutional neural network
Image classification refers to segregating pictures based on their visual characteristics. This
is a crucial area of study in computer vision and is widely used in various applications such …
is a crucial area of study in computer vision and is widely used in various applications such …
Enhanced prediction of dissolved oxygen concentration using a hybrid deep learning approach with sinusoidal geometric mode decomposition
W Li, Z Dong, T Chen, F Wang, F Huang - Water, Air, & Soil Pollution, 2024 - Springer
Dissolved Oxygen (DO) is a crucial indicator in water bodies, enabling assessment of
eutrophication degree, ecosystem status, self-purification capacity, and water quality health …
eutrophication degree, ecosystem status, self-purification capacity, and water quality health …
Vitis Vinera L. Leaf Detection using Faster R-CNN
Grapes are a type of vine that belongs to the Vitaceae family and has many health benefits.
There are dozens of grape varieties that are widespread in Indonesia. Grape varieties can …
There are dozens of grape varieties that are widespread in Indonesia. Grape varieties can …
Using AI and Big Data in the HealthCare Sector to help build a Smarter and more Intelligent HealthCare System
SK Marimekala, J Lamb, R Epstein… - 2024 IEEE World AI …, 2024 - ieeexplore.ieee.org
The purpose of this paper is to demonstrate the use of AI and Big Data in HealthCare Sector
to help build a smarter and more intelligent HealthCare System. Researchers in HealthCare …
to help build a smarter and more intelligent HealthCare System. Researchers in HealthCare …
Evaluation of geomorphological classification uncertainty using rough set theory: A case study of Shaanxi Province, China
Geomorphological classification is affected by classification principles, indicators, methods,
and data resolution, which can lead to uncertainty in the results. Such uncertainty directly …
and data resolution, which can lead to uncertainty in the results. Such uncertainty directly …