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Development of an Operational Algorithm for Automated Deforestation Mapping via the Bayesian Integration of Long-Term Optical and Microwave Satellite Data
2019
Remote Sensing
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In this paper, we introduce an operational algorithm for automated deforestation mapping using long-term optical and L-band SAR data, including a simple time-series analysis of Landsat stacks and a multilayered ...
Although the sensor integration of optical and microwave sensors is of compelling research interest, particularly in the conduct of deforestation monitoring, this topic has not been widely studied. ...
We also thank the GEE for archiving Landsat series and Sentinel-2 data.
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/rs11172038
fatcat:v3okegudanh55f3egx3hab4fyq
Historical background and current developments for mapping burned area from satellite Earth observation
2019
Remote Sensing of Environment
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With the launch of the first Earth observation satellites, remote sensing quickly became a more practical alternative to detect burned areas, as they provide timely regional and global coverage of fire ...
recent approaches to map burned areas and evaluates the existing burned area products (both at global and regional scales). ...
However, an automated algorithm for nationwide BA and severity mapping using Geoscience Australia's (GA's) Landsat and Sentinel-2 data cube infrastructure was recently developed and is close to operational ...
doi:10.1016/j.rse.2019.02.013
fatcat:eywm3ix63ngvdczdtdci46etwu
Deep Learning and Earth Observation to Support the Sustainable Development Goals
[article]
2021
arXiv
pre-print
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This paper reviews current deep learning approaches for Earth observation data, along with their application towards monitoring and achieving the SDGs most impacted by the rapid development of deep learning ...
Exciting times ahead are coming where algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development. ...
Also, an active research field is the joining of these two worlds via active learning [270] algorithms: by allowing interactive back and forth between the annotators and the DL models, significant speedups ...
arXiv:2112.11367v1
fatcat:7eve5dr45vcublfqyzzrccuvxa
Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications
2021
Remote Sensing
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This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special ...
The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. ...
Data Availability Statement: Not applicable. Acknowledgments: The authors sincerely thank Airbus Defense and Space UK and Catapult SA for providing NovaSAR data from the commissioning phase. ...
doi:10.3390/rs13040604
fatcat:zgqmk5chjbc7hio5l6serraeym
An overview of MATISSE-v2.0
2010
Optics in Atmospheric Propagation and Adaptive Systems XIII
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The symposium, like our other conferences and activities, would not be possible without the dedicated contribution of our participants and members. ...
This Programme is based on commitments received up to the time of publication and is subject to change without notice. ...
for providing the Radarsat-2, TerraSAR-X and weather data respectively. ...
doi:10.1117/12.868183
fatcat:5anlqspzzzcftfufaxvlr45zve
Table of Contents
2020
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
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INFORMATION CONTENT ALGORITHM FOR AUTOMATED ........................................ AN IMPROVED TARGET EXTRACTION SCHEME FOR FORWARD-LOOKING ............................. ...
AND OPTICAL REMOTE SENSING TU2-R17.1: INVESTIGATION OF THE FMASK CLOUD MASKING ALGORITHM USING ...................................... 3432 SIMULATED MULTISPECTRAL DATA Robert Sundberg, Spectral Sciences ...
doi:10.1109/igarss39084.2020.9323828
fatcat:6aittajt35gufeaugcmemu5cya
Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study of Ecosystem Functioning and Conservation at Large Spatial Scales
[chapter]
2020
Remote Sensing of Plant Biodiversity
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However, RS data comes with limitation of their own, and despite of the many opportunities offered by RS data, certain aspects and scales of biodiversity are currently not measurable using RS technology ...
Thus, the combination of RS, in-situ and other auxiliary data, provides the most powerful approach to assessing ecosystem functioning and conservation at large spatial scales. ...
Acknowledgments FS was supported by an Anne McLaren fellowship by the University of Nottingham (UK). We thank Nathalie Pettorelli for useful discussions and suggestions. ...
doi:10.1007/978-3-030-33157-3_17
fatcat:f3qfvseedjdqhgs7xzpc3wf6u4
Multi sensor validation and error characteristics of Arctic satellite sea surface temperature observations
2012
Remote Sensing of Environment
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The lake processor is a modification of the processor built during the ATSR Reprocessing for Climate (ARC) project, that was funded by the UK Natural We will present the background to this approach and ...
Acknowledgement This study was supported by the project "Research for the Meteorological Observation Technology and Its Application" at National Institute of Meteorological Research, KMA. ...
It requires high accuracy and stability of satellite data for long-term climate data record of global SST. ...
doi:10.1016/j.rse.2012.01.013
fatcat:e64zv2bspzh7pkiaqqx76humiu
NASA workshop on issues in the application of data mining to scientific data
2000
SIGKDD Explorations
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The objectives of the workshop were to bring together computer scientists and physical scientists to assess the state of the practice in data mining for scientific applications; share experiences in applying ...
this technology; and to help determine the direction of future work in data mining. ...
-Data mining is one of these--develop loss functions for unusual features and look for data mining methodologies that look for features based on the loss function. This is implicitly Bayesian. ...
doi:10.1145/360402.360427
fatcat:5b6upxenlvfmfmqsk6xhjtynvm
Knowledge Extracted from Copernicus Satellite Data
2019
Zenodo
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During the development of deep learning algorithms, a key activity is to establish a large amount of referenced Earth Observation data. ...
In this publication, we focus on the Polar case which requires the selection of validation areas, the generation of a training dataset, the development and testing of deep learning algorithms, and the ...
Hope that through our practices to promote the capacity of development, integration and utilization of marine data resources. ...
doi:10.5281/zenodo.3941573
fatcat:zzifwgljifck5bpjnboetsftfu
Conservation Tech Directory
[article]
2022
figshare.com
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*An interactive version of this directory is available at conservationtech.directory*A list of organizations, communities, companies, open-source projects and other resources within the conservation technology ...
She builds machine learning platforms to support wildlife research, largely focused on automating image processing and bioacoustics analysis for marine mammal research. ...
ear tags for large mammals; satellite remote data download http://cerestag.com/ sensing tech for monitoring tropical deforestation & forest degradation; automated system for converting satellite imagery ...
doi:10.6084/m9.figshare.15442200.v12
fatcat:b4bfumzexjec5lh4bdk3q35bia
Conservation Tech Directory
[article]
2022
figshare.com
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*An interactive version of this directory is available at conservationtech.directory*A list of organizations, communities, companies, open-source projects and other resources within the conservation technology ...
She builds machine learning platforms to support wildlife research, largely focused on automating image processing and bioacoustics analysis for marine mammal research. ...
Netherlands provides server-side components to run participatory mapping projects; can create custom data structures; integrate pictures & videos with maps; access data via open REST API for data collection ...
doi:10.6084/m9.figshare.15442200.v13
fatcat:qgs2d2chsbdo5hflfzwu3p2rzq
Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing
2022
Sensors
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An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed. ...
Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. ...
Kappa Coefficient KNN K-Nearest Neighbors Classifier LORSAL Logistic Regression via Variable Splitting and Augmented Lagrangian LSTM Long Short-Term Memory MA Mapping Accuracy MAE Mean Absolute Error MAPE ...
doi:10.3390/s22062416
pmid:35336587
pmcid:PMC8949619
fatcat:yik4lq6xq5afvbrsluso7zdroq
Measuring atmospheric composition change
2009
Atmospheric Environment
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The variability of the atmospheric system and the extreme complexity of the atmospheric cycles for short-lived gaseous and aerosol species have led to the development of complex models to interpret observations ...
The validation of numerical models requires accurate information concerning the variability of atmospheric composition for targeted species via comparison with observations and measurements. ...
We are thankful to Mariana Berthet and the ACCENT project office for their support in the preparation of the article. ...
doi:10.1016/j.atmosenv.2009.08.020
fatcat:45zeml2sk5dizbnaydexgmtrhy
Emissions Trading
[chapter]
2017
Encyclopedia of GIS
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In terms of processing and delivery, digital platforms (satellite and aerial) will soon be able to deliver data nearly on-demand as processing routines for image data, regardless of source, approach near ...
Finally, in the Bayesian framework, it is necessary to define prior distributions for each of the data and process parameters. ...
Figure 2 demonstrates an example of aggregating counts of n-gram subsequences from check-in location sequence collection of all users. For instance, the subsequence l 1 ! ...
doi:10.1007/978-3-319-17885-1_100350
fatcat:qtqeulswn5fanpkosuygq57viq
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