Google Earth Engine and artificial intelligence (AI): a comprehensive review
Remote sensing (RS) plays an important role gathering data in many critical domains (eg,
global climate change, risk assessment and vulnerability reduction of natural hazards …
global climate change, risk assessment and vulnerability reduction of natural hazards …
UAV & satellite synergies for optical remote sensing applications: A literature review
E Alvarez-Vanhard, T Corpetti, T Houet - Science of remote sensing, 2021 - Elsevier
Unmanned aerial vehicles (UAVs) and satellite constellations are both essential Earth
Observation (EO) systems for monitoring land surface dynamics. The former is frequently …
Observation (EO) systems for monitoring land surface dynamics. The former is frequently …
A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection
In the task of change detection (CD), high-resolution remote sensing images (HRSIs) can
provide rich ground object information. However, the interference from noise and complex …
provide rich ground object information. However, the interference from noise and complex …
Image retrieval from remote sensing big data: A survey
The blooming proliferation of aeronautics and astronautics platforms, together with the ever-
increasing remote sensing imaging sensors on these platforms, has led to the formation of …
increasing remote sensing imaging sensors on these platforms, has led to the formation of …
Air quality predictions with a semi-supervised bidirectional LSTM neural network
L Zhang, P Liu, L Zhao, G Wang, W Zhang… - Atmospheric Pollution …, 2021 - Elsevier
Efficient and accurate air quality predictions can contribute to public health protection and
policy decision making. Fine particulate matter (PM 2.5) is an important index for measuring …
policy decision making. Fine particulate matter (PM 2.5) is an important index for measuring …
A survey on active deep learning: from model driven to data driven
Which samples should be labelled in a large dataset is one of the most important problems
for the training of deep learning. So far, a variety of active sample selection strategies related …
for the training of deep learning. So far, a variety of active sample selection strategies related …
[HTML][HTML] An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing
E Izquierdo-Verdiguier, R Zurita-Milla - International Journal of Applied …, 2020 - Elsevier
New Earth observation missions and technologies are delivering large amounts of data.
Processing this data requires developing and evaluating novel dimensionality reduction …
Processing this data requires developing and evaluating novel dimensionality reduction …
Landslide susceptibility prediction considering neighborhood characteristics of landslide spatial datasets and hydrological slope units using remote sensing and GIS …
Landslides are affected not only by their own environmental factors, but also by the
neighborhood environmental factors and the landslide clustering effect, which are …
neighborhood environmental factors and the landslide clustering effect, which are …
Sentinel-1-imagery-based high-resolution water cover detection on wetlands, Aided by Google Earth Engine
Saline wetlands experience large temporal fluctuations in water supply during the year and
are recharged only or mainly through precipitation, meaning they are vulnerable to climate …
are recharged only or mainly through precipitation, meaning they are vulnerable to climate …
Road extraction from very-high-resolution remote sensing images via a nested SE-Deeplab model
Y Lin, D Xu, N Wang, Z Shi, Q Chen - Remote sensing, 2020 - mdpi.com
Automatic road extraction from very-high-resolution remote sensing images has become a
popular topic in a wide range of fields. Convolutional neural networks are often used for this …
popular topic in a wide range of fields. Convolutional neural networks are often used for this …