Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects
Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …
management, environmental modelling and assessment, and agricultural production …
LANet: Local attention embedding to improve the semantic segmentation of remote sensing images
The trade-off between feature representation power and spatial localization accuracy is
crucial for the dense classification/semantic segmentation of remote sensing images (RSIs) …
crucial for the dense classification/semantic segmentation of remote sensing images (RSIs) …
An object-based convolutional neural network (OCNN) for urban land use classification
Urban land use information is essential for a variety of urban-related applications such as
urban planning and regional administration. The extraction of urban land use from very fine …
urban planning and regional administration. The extraction of urban land use from very fine …
Avoiding negative transfer for semantic segmentation of remote sensing images
H Wang, C Tao, J Qi, R **_individual_plants_in_a_highly_diverse_high-elevation_ecosystem_using_UAV_imagery_and_deep_learning/links/5f7f04f392851c14bcb6a763/Identifying-and-map**-individual-plants-in-a-highly-diverse-high-elevation-ecosystem-using-UAV-imagery-and-deep-learning.pdf" data-clk="hl=hr&sa=T&oi=gga&ct=gga&cd=9&d=14437855640868094031&ei=i7KvZ8WFF4C96rQP29mI6AY" data-clk-atid="TwBQ2DWRXcgJ" target="_blank">[PDF] researchgate.net
Identifying and map** individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning
The identification and counting of plant individuals is essential for environmental monitoring.
UAV based imagery offer ultra-fine spatial resolution and flexibility in data acquisition, and …
UAV based imagery offer ultra-fine spatial resolution and flexibility in data acquisition, and …