[HTML][HTML] Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework
Climate change and rapid urbanisation exacerbated multiple urban issues threatening
urban sustainability. Numerous studies integrated machine learning and remote sensing to …
urban sustainability. Numerous studies integrated machine learning and remote sensing to …
[HTML][HTML] Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis
Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …
learning methods have shifted the paradigm of image classification from pixel-based and …
Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities
The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …
promises significant advances to support the Sustainable Development Goals (SDGs). New …
[HTML][HTML] Object detection and image segmentation with deep learning on Earth observation data: A review—Part II: Applications
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …
investigating aggregated classes. The increase in data with a very high spatial resolution …
Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images
Abstract The Local Climate Zone (LCZ) scheme is a classification system providing a
standardization framework to present the characteristics of urban forms and functions …
standardization framework to present the characteristics of urban forms and functions …
[HTML][HTML] Enabling country-scale land cover mapping with meter-resolution satellite imagery
High-resolution satellite images can provide abundant, detailed spatial information for land
cover classification, which is particularly important for studying the complicated built …
cover classification, which is particularly important for studying the complicated built …
Deep-learning-based multispectral satellite image segmentation for water body detection
K Yuan, X Zhuang, G Schaefer, J Feng… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Automated water body detection from satellite imagery is a fundamental stage for urban
hydrological studies. In recent years, various deep convolutional neural network (DCNN) …
hydrological studies. In recent years, various deep convolutional neural network (DCNN) …
[图书][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …
learning in the field of earth sciences, from four leading voices Deep learning is a …
[HTML][HTML] Deep learning segmentation and classification for urban village using a worldview satellite image based on U-Net
Z Pan, J Xu, Y Guo, Y Hu, G Wang - Remote Sensing, 2020 - mdpi.com
Unplanned urban settlements exist worldwide. The geospatial information of these areas is
critical for urban management and reconstruction planning but usually unavailable …
critical for urban management and reconstruction planning but usually unavailable …