The dataset includes multimodal annotated data for remote sensing of Maya archaeology and is suitable for deep learning. The dataset covers the area around Chactún, one of the largest ancient Maya urban centres in the central Yucatán peninsula.It includes five types of data:high-resolution airborne laser scanning (ALS, lidar) data visualisations (sky view factor, positive openness, slope),high-resolution airborne laser scanning derived canopy height model,Sentinel-1 Short Aperture Radar (SAR) satellite data (yearly average Sigma0),Sentine-2 optical satellite data (12 bands + cloud mask, 17 dates), andmanual data annotations.The manual annotations (used as binary masks) represent three different types of ancient Maya structures (class labels: buildings, platforms, and aguadas – artificial reservoirs) within the study area, their exact locations, and boundaries.The dataset is ready for use with convolutional neural networks (CNNs) for object recognition, object localization (detection), and semantic segmentation. The dataset has already been used for the Discover the Mysteries of the Maya computer vision competition.We would like to provide this dataset to help more research teams develop their own computer vision models for investigations of Maya archaeology or improve existing ones.A detailed description of the datasets has been published by Kokalj, Ž., Džeroski, S., Šprajc, I. et al. Machine learning-ready remote sensing data for Maya archaeology. Scientific Data 10, 558 (2023). https://doi.org/10.1038/s41597-023-02455-xThe authors and institutions they are affiliated with exclude all liability for any reliance on the data.

1. Light detection and ranging (LiDAR) technology provides ecologists with high-resolution data on three-dimensional vegetation structure. Large LiDAR datasets challenge predictive ecologists, who commonly simplify point clouds into structural attributes (namely LiDAR-based metrics such as canopy height), which are used as predictors in ecological models, potentially with loss of relevant information. 2. We illustrate an efficient alternative approach to reduce the dimensionality of LiDAR data that aims at minimal data filtering with no a priori assumptions on the ecology of the target species. We first fit the ecological model exploiting the full variability of the LiDAR point cloud, then we explain the results using post-modelling LiDAR-data classification for ecological interpretation only. This is the classical logic of explorative, hypothesis generating and predictive statistics, rather than testing specific vegetation-structural hypotheses. 3. First, we reduce the dimensionality of the LiDAR point cloud by Principal Component Analysis (PCA) to fewer predictors. Secondly, we show that LiDAR-PCs are capable to outperforming commonly used environmental predictors in ecological modelling, including LiDAR-based metrics. We exemplify this by modelling red deer (Cervus elaphus) and roe deer (Capreolus capreolus) resource selection in the Bavarian Forest National Park, Germany. After fitting the ecological model, we provide an interpretation of the information included in LiDAR-PCs, which allows users to draw conclusions whenever using them as predictors. We make use of the PCA rotation matrix and post-modelling data classification, and document deer selection for understory vegetation at unprecedented fine scale. 4. Our approach is the first attempt in animal ecology to avoid the use of LiDAR-based metrics as model predictors, but rather generate principal components able to capture most of the LiDAR point cloud variability. Our study demonstrates that LiDAR-PCs can boost ecological models. We envision a potential use of LiDAR-PCs in several applications, particularly species distribution and habitat suitability models. We demonstrate an application of our approach by building suitability maps for both deer species, which can be used by practitioners to visualize model spatial predictions and understand the type of forest structures selected by deer.

LITE_L1 data are LIDAR Vertical profile data along the orbital flight path of STS-64.Lidar In-Space Technology Experiment (LITE) used a three-wavelength (355 nm, 532 nm and 1064 nm) backscatter lidar which flew on the space shuttle Discovery as part of the STS-64 mission between September 9 and September 20, 1994. The LITE instrument was designed with the capability to make measurements of clouds, aerosols in the stratosphere and troposphere, the height of the planetary boundary layer, and atmospheric temperature and density in the stratosphere between 25 km and 40 km altitude. Additionally, limited measurements of the surface return strength over both land and ocean were collected to explore retrievals of surface properties.The LITE data were transmitted real time the by Ku-band system through TDRSS downlink to the LITE operations center at JSC. There was a gap in the high-rate coverage between 60 E and 85 E due to the zone of exclusion, where neither TDRSS satellite was in view. Additional random gaps in the data occurred due to telemetry dropouts during data transmission.The LITE L1 data product was formed by processing and reformatting the LITE high-rate telemetry data. The LITE L1 processing steps included:Correcting the profiles for instrument artifacts. Subtracting the DC offset from each lidar profile. Interpolating lidar profiles to a geolocated, common altitude grid, which extends from -4.985 to 40.0 km with a 15 m vertical resolution. Determining the LITE system calibration constants for the 355 nm and 532 nm wavelength profiles.Merged with the LITE L1 lidar profiles are: Identification Parameters, Time Parameters, Location Parameters, Operation Mode Parameters, Validity Flags, Measurement Location Descriptions, Temperature and Pressure Profiles Derived from NMC Data, Instrument Status Information.The archived files are concatenations of about 1000 (depending on data gaps) sets of headers and profiles. Read software programs written in C or IDL are available.

The EarthScope Northern California Lidar project acquired high resolution airborne laser swath mapping imagery along major active faults as part of the EarthScope Facility project funded by the National Science Foundation (NSF). Between this project and the previously conducted B4 project, also funded by NSF, the entire San Andreas fault system has now been imaged with high resolution airborne lidar, along with many other important geologic features. EarthScope is funded by NSF and conducted in partnership with the USGS and NASA. GeoEarthScope is a component of EarthScope that includes the acquisition of aerial and satellite imagery and geochronology. EarthScope is managed at UNAVCO. Please use the following language to acknowledge EarthScope Lidar: This material is based on services provided to the Plate Boundary Observatory by NCALM (http://www.ncalm.org). PBO is operated by UNAVCO for EarthScope (http://www.earthscope.org) and supported by the National Science Foundation (No. EAR-0350028 and EAR-0732947).

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.

This web map allows for the download of KyFromAbove LiDAR data by 5k tile in LAZ format. This point cloud data was acquired during the typical leaf-off acquisition period (winter-spring) over a period of several years and may be provided as LAS version 1.1, 1.2, or 1.4 depending upon the acquisition period. Users will need to download the LAZIP.exe in order to decompress each tile. LiDAR data specifications adopted by the KyFromAbove Technical Advisory Committee can be found here. This is the source data used to create the Commonwealth's 5 foot digital elevation model (DEM) and its associated derivatives. More information regarding this data resource can be found on the KyGeoPortal.

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.

This dataset is part of the larger data collection, “Aerial imagery object identification dataset for building and road detection, and building height estimation”, linked to in the references below and can be accessed here: https://dx.doi.org/10.6084/m9.figshare.c.3290519. For a full description of the data, please see the metadata: https://dx.doi.org/10.6084/m9.figshare.3504413.

Imagery data from the United States Geological Survey (USGS); building and road shapefiles are from OpenStreetMaps (OSM) (these OSM data are made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/); and the Lidar data are from U.S. National Oceanic and Atmospheric Administration (NOAA), the Texas Natural Resources Information System (TNRIS).

LiDAR (Light Detection and Ranging) is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From satellite, aeroplane or helicopter, a LiDAR system sends a light pulse to the ground. This pulse hits the ground and returns back to a sensor on the system. The time is recorded to measure how long it takes for this light to return. Knowing this time measurement scientists are able to create topography maps.

LiDAR data are collected as points (X,Y,Z (x & y coordinates) and z (height)). The data is then converted into gridded (GeoTIFF) data to create a Digital Terrain Model and Digital Surface Model of the earth. This LiDAR data was collected between 2015 and 2020.

Digital Terrain Models (DTM) are bare earth models (no trees or buildings) of the Earth’s surface.

Digital Surface Models (DSM) are earth models in its current state. For example, a DSM includes elevations from buildings, tree canopy, electrical power lines and other features.

This data was collected by the Geological Survey Ireland, the Department of Culture, Heritage and the Gaeltacht, the Discovery Programme, the Heritage Council, Transport Infrastructure Ireland, New York University, the Office of Public Works and Westmeath County Council. All data formats are provided as GeoTIFF rasters but are at different resolutions. Data resolution varies depending on survey requirements. Resolutions for each organisation are as follows:

GSI – 1m

DCHG/DP/HC - 0.13m, 0.14m, 1m

NY – 1m

TII – 2m

OPW – 2m

WMCC - 0.25m

Both a DTM and DSM are raster data. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. The grid cell size varies depending on the organisation that collected it. GSI data has a grid cell size of 1 meter by 1 meter. This means that each cell (pixel) represents an area of 1 meter squared.

Digital elevation data which describes Australia's landforms and seabed is crucial for addressing issues relating to the impacts of climate change, disaster management, water security, environmental management, urban planning and infrastructure design. In recent years dramatic developments in LiDAR technology and industry capabilities have revolutionised our ability to address these issues at the local level. However, inconsistent and diverse product specifications, and variable data quality are often making it difficult to integrate datasets to address regional, state and national issues. In order to optimise investment and the utility of both existing and future data collections there is a need for a national base specification which defines a consistent set of minimum products which ensure compatibility across projects and States.

LiDAR (Light Detection and Ranging) is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From satellite, aeroplane or helicopter, a LiDAR system sends a light pulse to the ground. This pulse hits the ground and returns back to a sensor on the system. The time is recorded to measure how long it takes for this light to return. Knowing this time measurement scientists are able to create topography maps.LiDAR data are collected as points (X,Y,Z (x & y coordinates) and z (height)). The data is then converted into gridded (GeoTIFF) data to create a Digital Terrain Model and Digital Surface Model of the earth. This LiDAR data was collected between June and October 2018.This data shows the areas in Ireland for which you can download LiDAR data and contains links to download the data. This is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The LiDAR coverage is shown as polygons. Each polygon is 2000m by 2000m in size and holds information on: the location, county, data provider, owner, licence, published date, capture date, surveyor, RMS error, resolution and a link to download the LiDAR raster data in 2000m by 2000m sections.

LiDAR (Light Detection and Ranging) is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From satellite, aeroplane or helicopter, a LiDAR system sends a light pulse to the ground. This pulse hits the ground and returns back to a sensor on the system. The time is recorded to measure how long it takes for this light to return.Knowing this time measurement scientists are able to create topography maps.LiDAR data are collected as points (X,Y,Z (x & y coordinates) and z (height)). The data is then converted into gridded (GeoTIFF) data to create a Digital Terrain Model and Digital Surface Model of the earth. This LiDAR data was collected between Oct.2006 and Jan. 2007. This data shows the areas in Ireland for which you can download LiDAR data and contains links to download the data. This is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The LiDAR coverage is shown as polygons. Each polygon is 2000m by 2000m in size and holds information on: the location, data provider, owner, licence, published date, capture date, surveyor, RMS error, resolution and a link to download the LiDAR raster data in 2000m by 2000m sections.

The EarthScope Southern & Eastern California Lidar Project acquired high resolution lidar topography data along major active faults as part of the EarthScope Facility project. EarthScope is funded by NSF and conducted in partnership with the USGS and NASA. GeoEarthScope is a component of EarthScope that includes the acquisition of aerial and satellite imagery and geochronology. EarthScope is managed at UNAVCO. Please use the following language to acknowledge EarthScope Lidar: This material is based on services provided to the Plate Boundary Observatory by NCALM (https://ncalm.cive.uh.edu/). PBO is operated by UNAVCO for EarthScope (http://www.earthscope.org) and supported by the National Science Foundation (No. EAR-0350028 and EAR-0732947). Publications associated with this dataset can be found at NCALM's Data Tracking Center

ASIA-AQ_AircraftRemoteSensing_LaRC-G3_HSRL2_Data is the High Spectral Resolution Lidar (HSRL) data collected onboard the NASA LaRC G-III aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data collection for this product is complete.The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

LiDAR (Light Detection and Ranging) is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From satellite, aeroplane or helicopter, a LiDAR system sends a light pulse to the ground. This pulse hits the ground and returns back to a sensor on the system. The time is recorded to measure how long it takes for this light to return. Knowing this time measurement scientists are able to create topography maps.LiDAR data are collected as points (X,Y,Z (x & y coordinates) and z (height)). The data is then converted into gridded (GeoTIFF) data to create a Digital Terrain Model and Digital Surface Model of the earth. This LiDAR data was collected in 2007.This data shows the areas in Cork for which you can download LiDAR data and contains links to download the data. This is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The LiDAR coverage is shown as polygons. Each polygon is 2000m by 2000m in size and holds information on: the location, data provider, owner, licence, published date, capture date, surveyor, RMS error, resolution and a link to download the LiDAR raster data in 2000m by 2000m sections.

CAL_LID_L3_Cloud_Occurrence-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 3 Cloud Occurrence Data, Standard Version 1-00 data product. This data product was collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The degradation of the laser energies that started in September 2016 had a negative impact on the product, and because of this, generation and distribution ended in December 2016. Updated Lidar Level 2 data products and changes to the Lidar Level 3 Cloud Occurrence algorithm will need to be completed before a new release of this product is released.This product reports global distributions of clouds on a uniform spatial grid. All level 3 parameters are derived from the CALIPSO level 2 data, with a temporal average of one month. CALIPSO was launched on April 28, 2006, and continues to collect data necessary to study the impact of clouds and aerosols on the Earth's radiation budget and climate. It flies in the international A-Train constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES.

CAL_LID_L0-Standard-V1-00 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Lidar Level 0, Version 1-00 data product. These data product were collected using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). This data product translates a matched set of three 90-minute raw Lidar Level 0 binary files into a single file containing the lidar measurements and health and status data from all three channels; 532 nm parallel, 532 nm perpendicular, and 1064 nm. CALIPSO was a partnership between NASA and the French Space Agency, CNES. CALIPSO was launched on April 28, 2006 to study the many roles played by clouds and aerosols in Earth’s climate and weather. It flew in the international A-Train constellation for coincident Earth observations from launch until September 13, 2018, when CALIPSO began lowering its orbit from 705 km to 688 km (428 miles) above the Earth to resume formation flying with CloudSat as part of the “C-Train”. The CALIPSO satellite carried three remote sensing instruments: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field-of-View Camera (WFC). By mutual agreement between NASA and CNES, the CALIPSO science mission concluded on August 1, 2023.

Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science, especially for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) program. This project provides a 1-meter land-cover mapping of the CAP LTER study area (greater Phoenix metropolitan area and surrounding Sonoran desert). The mapping is generated primarily using 2015 National Agriculture Imagery Program (NAIP) four-band data, with auxiliary GIS data used to improve accuracy. Auxiliary data include the 2015 cadastral parcel data, the 2014 USGS LiDAR data (1-meter), the 2014 Microsoft/OpenStreetMap Building Footprint data, the 2015 Street TIGER/Line, and a previous (2010) NAIP-based land-cover map of the study area (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=623). Among auxiliary data, building footprints and LiDAR data significantly improved the boundary detection of above-ground objects. Post-classification, manual editing was applied to minimize classification errors. As a result, the land-cover map achieves an overall accuracy of 94 per cent. The map contains eight land cover classes, including: (1) building, (2) asphalt, (3) bare soil and concrete, (4) tree and shrub, (5) grass, (6) water, (7) active cropland, and (8) fallow. When compared to the aforementioned, previous (2010) NAIP-based land-cover map for the study area, buildings and tree canopies are classified more accurately in this 2015 land-cover map.

CAL_LID_L2_05kmCPro-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Cloud Profile, Version 4-51 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.

NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover