On the public citation graph

Annual Earth-observation embeddings encode wildfire disturbance and support simplified burned area mapping

https://doi.org/10.2139/ssrn.7507417
0 signed · 32 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.ecolind.2022.108999openalex-snapshot
Burnt-Net: Wildfire burned area mapping with single post-fire Sentinel-2 data and deep learning morphological neural network
inferredcites →https://doi.org/10.1016/j.isprsjprs.2022.12.026openalex-snapshot
Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models
inferredcites →https://doi.org/10.1016/j.isprsjprs.2023.07.002openalex-snapshot
Burned area semantic segmentation: A novel dataset and evaluation using convolutional networks
inferredcites →https://doi.org/10.1016/j.jag.2013.05.014openalex-snapshot
Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments
inferredcites →https://doi.org/10.1016/j.jag.2017.09.011openalex-snapshot
Burned area detection based on Landsat time series in savannas of southern Burkina Faso
inferredcites →https://doi.org/10.1016/j.jag.2019.02.010openalex-snapshot
How well do global burned area products represent fire patterns in the Brazilian Savannas biome? An accuracy assessment of the MCD64 collections
inferredcites →https://doi.org/10.1016/j.jag.2022.102761openalex-snapshot
Detecting and quantifying residue burning in smallholder systems: An integrated approach using Sentinel-2 data
inferredcites →https://doi.org/10.1016/j.rse.2005.09.012openalex-snapshot
Spatiotemporal problems with detecting and mapping mosaic fire regimes with coarse-resolution satellite data in savanna environments
inferredcites →https://doi.org/10.1016/j.rse.2014.01.008openalex-snapshot
Validation of the 2008 MODIS-MCD45 global burned area product using stratified random sampling
inferredcites →https://doi.org/10.1016/j.rse.2014.01.011openalex-snapshot
Continuous change detection and classification of land cover using all available Landsat data
inferredcites →https://doi.org/10.1016/j.rse.2016.07.022openalex-snapshot
A MODIS-based burned area assessment for Russian croplands: Mapping requirements and challenges
inferredcites →https://doi.org/10.1016/j.rse.2018.08.005openalex-snapshot
The Collection 6 MODIS burned area mapping algorithm and product
inferredcites →https://doi.org/10.1016/j.rse.2018.12.011openalex-snapshot
Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa
inferredcites →https://doi.org/10.1016/j.rse.2019.111490openalex-snapshot
Global validation of the collection 6 MODIS burned area product
inferredcites →https://doi.org/10.1016/j.rse.2023.113918openalex-snapshot
Need and vision for global medium-resolution Landsat and Sentinel-2 data products
inferredcites →https://doi.org/10.1016/j.rse.2025.115006openalex-snapshot
The NASA VIIRS burned area product, global validation, and intercomparison with the NASA MODIS burned area product
inferredcites →https://doi.org/10.1038/s41893-024-01291-0openalex-snapshot
Wildfire risk for global wildland–urban interface areas
inferredcites →https://doi.org/10.1071/9780643104839openalex-snapshot
Flammable Australia
inferredcites →https://doi.org/10.1093/biosci/bix014openalex-snapshot
An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm
inferredcites →https://doi.org/10.1109/jstars.2024.3381737openalex-snapshot
FLOGA: A Machine-Learning-Ready Dataset, a Benchmark, and a Novel Deep Learning Model for Burnt Area Mapping With Sentinel-2
inferredcites →https://doi.org/10.1109/jstars.2024.3402122openalex-snapshot
Burned Area Segmentation in Optical Remote Sensing Images Driven by U-Shaped Multistage Masked Autoencoder
inferredcites →https://doi.org/10.1109/jstars.2024.3426044openalex-snapshot
Using Barlow Twins to Create Representations From Cloud-Corrupted Remote Sensing Time Series
inferredcites →https://doi.org/10.1109/tgrs.2019.2943901openalex-snapshot
Assessing the Shape Accuracy of Coarse Resolution Burned Area Identifications
inferredcites →https://doi.org/10.1126/science.aal4108openalex-snapshot
A human-driven decline in global burned area
inferredcites →https://doi.org/10.3390/fire8040126openalex-snapshot
Spatial Agreement of Burned Area Products Derived from Very High to Coarse-Resolution Satellite Imagery in African Biomes
inferredcites →https://doi.org/10.3390/fire8110422openalex-snapshot
Including Small Fires in Global Historical Burned Area Products: Promising Results from a Landsat-Based Product
inferredcites →https://doi.org/10.3390/rs11050489openalex-snapshot
30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine
inferredcites →https://doi.org/10.3390/rs12152422openalex-snapshot
A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data
inferredcites →https://doi.org/10.3390/rs8120986openalex-snapshot
The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests
inferredcites →https://doi.org/10.5194/essd-13-5353-2021openalex-snapshot
Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning
inferredcites →https://doi.org/10.5194/essd-9-697-2017openalex-snapshot
Global fire emissions estimates during 1997–2016
inferredcites →https://doi.org/10.5194/nhess-10-485-2010openalex-snapshot
Assessment and validation of wildfire susceptibility and hazard in Portugal
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.