Weirather et al., 2018 - Google Patents
Automated Delineation Of Wildfire Areas Using Sentinel-2 Satellite ImageryWeirather et al., 2018
- Document ID
- 4677151889127659349
- Author
- Weirather M
- Zeug G
- Schneider T
- Publication year
- Publication venue
- GI_Forum 2018
External Links
Snippet
Climate change will bring many changes to the world. For example, the frequency and severity of natural hazards and related disasters are expected to increase globally. Wildfires already affect thousands of people every year and cause billions of Euros' worth of damage …
- 238000004422 calculation algorithm 0 abstract description 19
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/20—Image acquisition
- G06K9/2018—Identifying/ignoring parts by sensing at different wavelengths
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance, e.g. risk analysis or pensions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Mondejar et al. | Near infrared band of Landsat 8 as water index: a case study around Cordova and Lapu-Lapu City, Cebu, Philippines | |
Argañaraz et al. | Assessing wildfire exposure in the Wildland-Urban Interface area of the mountains of central Argentina | |
Thomas et al. | Validation of North American forest disturbance dynamics derived from Landsat time series stacks | |
Fraser et al. | A method for detecting large-scale forest cover change using coarse spatial resolution imagery | |
Xin et al. | Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data | |
CN102542248B (en) | Automatic detection of fires on earth's surface and of atmospheric phenomena such as clouds, veils, fog or the like, by means of a satellite system | |
Zhang et al. | Monthly burned area and forest fire carbon emission estimates for the Russian Federation from SPOT VGT | |
Souza Jr et al. | Combining spectral and spatial information to map canopy damage from selective logging and forest fires | |
Shimada et al. | New global forest/non-forest maps from ALOS PALSAR data (2007–2010) | |
US20160048925A1 (en) | Method of determining structural damage using positive and negative tree proximity factors | |
Lizundia-Loiola et al. | Global burned area mapping from Sentinel-3 Synergy and VIIRS active fires | |
Koltunov et al. | On timeliness and accuracy of wildfire detection by the GOES WF-ABBA algorithm over California during the 2006 fire season | |
Fensholt et al. | Analysing the advantages of high temporal resolution geostationary MSG SEVIRI data compared to Polar Operational Environmental Satellite data for land surface monitoring in Africa | |
Reimer et al. | Advancing reference emission levels in subnational and national REDD+ initiatives: a CLASlite approach | |
Pu et al. | A dynamic algorithm for wildfire mapping with NOAA/AVHRR data | |
Kuhnell et al. | Mapping woody vegetation cover over the state of Queensland using Landsat TM imagery | |
He et al. | Enhancement of a fire detection algorithm by eliminating solar reflection in the mid-IR band: Application to AVHRR data | |
Farhadi et al. | Badi: a novel burned area detection index for sentinel-2 imagery using google earth engine platform | |
Zidane et al. | An improved algorithm for mapping burnt areas in the Mediterranean forest landscape of Morocco | |
Chung et al. | Wildfire damage assessment using multi-temporal Sentinel-2 data | |
Gülci et al. | Mapping wildfires using Sentinel 2 MSI and Landsat 8 imagery: spatial data generation for forestry | |
Hamilton et al. | Spectroscopic analysis for mapping wildland fire effects from remotely sensed imagery | |
Weirather et al. | Automated Delineation Of Wildfire Areas Using Sentinel-2 Satellite Imagery | |
Lee et al. | Detection of wildfire-damaged areas using kompsat-3 image: A case of the 2019 unbong mountain fire in busan, South Korea | |
CN111563472A (en) | Method and device for rapidly extracting tobacco plume forest land burned area |