Unmanned aerial vehicle for remote sensing applications—A review
H Yao, R Qin, X Chen - Remote Sensing, 2019 - mdpi.com
The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in
almost every application (eg, agriculture, forestry, and mining) that needs observed …
almost every application (eg, agriculture, forestry, and mining) that needs observed …
Deep learning in remote sensing: A comprehensive review and list of resources
Central to the looming paradigm shift toward data-intensive science, machine-learning
techniques are becoming increasingly important. In particular, deep learning has proven to …
techniques are becoming increasingly important. In particular, deep learning has proven to …
Deepglobe 2018: A challenge to parse the earth through satellite images
Abstract We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which
includes three public competitions for segmentation, detection, and classification tasks on …
includes three public competitions for segmentation, detection, and classification tasks 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 …
Understanding deep learning in land use classification based on Sentinel-2 time series
The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is
rapidly increasing. DL techniques have provided excellent results in applications ranging …
rapidly increasing. DL techniques have provided excellent results in applications ranging …
Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks
In this work, we investigate various methods to deal with semantic labeling of very high
resolution multi-modal remote sensing data. Especially, we study how deep fully …
resolution multi-modal remote sensing data. Especially, we study how deep fully …
Semantics for robotic mapping, perception and interaction: A survey
For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …
require a deeper understanding of the world in which they operate. In robotics and related …
Semantic segmentation of earth observation data using multimodal and multi-scale deep networks
This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-
wise scene labeling of Earth Observation images. Especially, we train a variant of the …
wise scene labeling of Earth Observation images. Especially, we train a variant of the …
UAVid: A semantic segmentation dataset for UAV imagery
Semantic segmentation has been one of the leading research interests in computer vision
recently. It serves as a perception foundation for many fields, such as robotics and …
recently. It serves as a perception foundation for many fields, such as robotics and …
Relation matters: Relational context-aware fully convolutional network for semantic segmentation of high-resolution aerial images
Most current semantic segmentation approaches fall back on deep convolutional neural
networks (CNNs). However, their use of convolution operations with local receptive fields …
networks (CNNs). However, their use of convolution operations with local receptive fields …