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 …
An overview of remote monitoring methods in biodiversity conservation
RG Kerry, FJP Montalbo, R Das, S Patra… - … Science and Pollution …, 2022 - Springer
Conservation of biodiversity is critical for the coexistence of humans and the sustenance of
other living organisms within the ecosystem. Identification and prioritization of specific …
other living organisms within the ecosystem. Identification and prioritization of specific …
A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency
Although remote sensing of active fires is well-researched, their early detection has received
less attention. Additionally, simple threshold approaches based on contextual statistical …
less attention. Additionally, simple threshold approaches based on contextual statistical …
Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model
Identifying spatial distribution of crop planting in large-scale is one of the most significant
applications of remote sensing imagery. As an active remote sensing system, synthetic …
applications of remote sensing imagery. As an active remote sensing system, synthetic …
[HTML][HTML] Learning U-Net without forgetting for near real-time wildfire monitoring by the fusion of SAR and optical time series
Wildfires are increasing in intensity and frequency across the globe due to climate change
and rising global temperature. Development of novel approach to Monitor wildfire …
and rising global temperature. Development of novel approach to Monitor wildfire …
Assessment of k-nearest neighbor and random forest classifiers for mapping forest fire areas in central portugal using landsat-8, sentinel-2, and terra imagery
AP Pacheco, JAS Junior, AM Ruiz-Armenteros… - Remote Sensing, 2021 - mdpi.com
Forest fires threaten the population's health, biomass, and biodiversity, intensifying the
desertification processes and causing temporary damage to conservation areas. Remote …
desertification processes and causing temporary damage to conservation areas. Remote …
Assessing burned areas in wildfires and prescribed fires with spectral indices and SAR images in the Margalla Hills of Pakistan
The extent of wildfires cannot be easily mapped using field-based methods in areas with
complex topography, and in those areas the use of remote sensing is an alternative. This …
complex topography, and in those areas the use of remote sensing is an alternative. This …
[HTML][HTML] Uni-temporal multispectral imagery for burned area mapping with deep learning
Accurate burned area information is needed to assess the impacts of wildfires on people,
communities, and natural ecosystems. Various burned area detection methods have been …
communities, and natural ecosystems. Various burned area detection methods have been …
A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems
This paper investigates the capability of the free synthetic aperture radar (SAR) Sentinel-1 (S-
1) C-band data for burned area mapping through unsupervised machine learning open …
1) C-band data for burned area mapping through unsupervised machine learning open …
[HTML][HTML] Rapid wildfire damage estimation using integrated object-based classification with auto-generated training samples from Sentinel-2 imagery on Google Earth …
Satellite data are essential during wildfires for understanding its adverse effects and
improving the effectiveness of rapid disaster management. However, existing techniques …
improving the effectiveness of rapid disaster management. However, existing techniques …