Random forest classification data developed from multitemporal Landsat 8 spectral data and phenology metrics for a subregion in Sonoran and Mojave Deserts, April 2013 – December 2020

These data were compiled for the creation of a continuous, transboundary land cover map of Bird Conservation Region 33, Sonoran and Mojave Deserts (BCR 33). Objective(s) of our study were to, 1) develop a machine learning (ML) algorithm trained to classify vegetation land cover using remote sensing spectral data and phenology metrics from 2013-2020, over a large subregion of the Sonoran and Mojave Deserts BCR, 2) Calibrate, validate, and refine the final ML-derived vegetation map using a collection of openly sourced remote sensing and ground-based ancillary data, images, and limited fieldwork, and 3) Harmonize a new transboundary classification system by expanding existing land cover mapping resources from the United...

Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: Arizona, Baja California, Botany, California, Ecology, All tags... Forestry, Geography, Hermosillo, Information Sciences, Landsat images, Mexico, Mojave Desert, Phoenix, Remote Sensing, Sonora, Sonoran Desert, Southwest, US-Mexico Transboundary region, USGS Science Data Catalog (SDC), United States, artificial intelligence, biogeography, biota, classification, conservation, data release, desert ecosystems, ecosystem management, imageryBaseMapsEarthCover, land use and land cover, machine learning, modeling, natural resource management, phenology, phenology metrics, random forest model, remote sensing, remote sensing spectral data, time series datasets, transboundary, vegetation, Fewer tags

Map feature extraction challenge training and validation data

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the map feature extraction challenge are provided here, as well as competition details and a baseline solution. The data were derived from published sources and are provided...

Categories: Data; Tags: AI, Artificial Intelligence, GIS, ML, North America, All tags... competition, critical mineral resources, digitization, economy, geological maps, geoscientificInformation, geospatial datasets, machine learning, modeling, resource assessment, tool development, topographic maps, training data, Fewer tags

Map georeferencing challenge training and validation data

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the map georeferencing challenge are provided here, as well as competition details and a baseline solution. The data were derived from published sources and are provided...

Categories: Data; Tags: AI, Artificial Intelligence, GIS, ML, North America, All tags... competition, critical mineral resources, digitization, economy, geological maps, geoscientificInformation, geospatial datasets, machine learning, modeling, resource assessment, tool development, topographic maps, training data, Fewer tags

Training and validation data from the AI for Critical Mineral Assessment Competition

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the competition are provided here, as well as competition details and baseline solutions. The data are derived from published sources and are provided to the public to...

Categories: Data; Tags: "Geography"], "Mineral Resources", AI, Artificial Intelligence, GIS, All tags... ML, North America, USGS Science Data Catalog (SDC), ["Economic Geology", competition, critical mineral resources, digitization, economy, geological maps, geoscientificInformation, geospatial datasets, machine learning, modeling, resource assessment, tool development, topographic maps, training data, Fewer tags

AI Supported Maintenance and Reliability System in Wind Energy Production

The paper describes the result of the research in the fields of supervision, failure detection and prognosis, control, maintenance planning and decision support performed for ensuring high level availability of wind turbines and wind farms. This activity is realized in the frame of the EU 7th Framework project ReliaWind: Reliability focused research on optimizing Wind Energy systems design, operation and maintenance: Tools, proof of concepts, guidelines & methodologies for a new generation. Wind turbines are relatively complex electro-mechanical systems, their smooth functioning is an important economical factor. The handling of this complexity is supported by various, applied artificial intelligence techniques...

Categories: Publication; Types: Citation; Tags: artificial intelligence, maintenance, supervision, wind energy