Dataset organized by the Open-Earth-Monitor (OEMC) project within the context of Hackathon 2023.

The dataset (both train and test) was produced by stratified sampling of the ground-truth data provided by LUCAS Survey, funded by the European Commission. The target land cover considered level-3 classes from the harmonized legend, resulting in 72 classes distributed over 5 years (2006, 2009, 2012, 2015, 2018):

All samples were overlaid with 416 raster spatial layers, including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation, air temperature), accessibility and distance maps (highways, water bodies, burned areas), digital terrain model (slope and elevation) and other existing maps (population count and snow covering). The result values were organized in columns, one for each spatial layers, which combined represent the feature space available for ML modeling.

Column names:

The columns are formed by six metadata fields separated by _:

Example: red_landsat.glad.ard_p50_30m_jun25_sep12

Metadata fields:

F1 - Variable name: red

F2 - Variable procedure including product name: landsat.glad.ard

F3 - Position in the probability distribution: p50

F4 - Spatial resolution: 30m

F5 - Start date: jun25

F6 - End date: sep12

Column description:

All the columns can be aggregated in six thematic groups according to F1 and F2:

Satellite images (spectral reflectance & vegetation indices):

blue_landsat.glad.ard_{..}: Quarterly time-series of Landsat blue band (Witjes et al., 2023)

blue_mod13q1_{..}: Monthly time-series of MOD13Q1 blue band (EarthData)

evi_mod13q1.stl.trend.ols.alpha_{..}: Alpha coefficient / intercept (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)

evi_mod13q1.stl.trend.ols.beta_{..}: Beta coefficient / trend (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)

evi_mod13q1.stl.trend_{..}: Deseasonalized monthly time-series (trend component of STL) for MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)

evi_mod13q1_{..}: Monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (EarthData)

green_landsat.glad.ard_{..}: Quarterly time-series of Landsat green band (Witjes et al., 2023)

mir_mod13q1_{..}: Monthly time-series of MOD13Q1 mid-infrared band (EarthData)

ndvi_mod13q1_{..}: Monthly time-series of MOD13Q1 normalized vegetation index (NDVI) (EarthData)

nir_landsat.glad.ard_{..}: Quarterly time-series of Landsat near-infrared band (Witjes et al., 2023)

nir_mod13q1_{..}: Monthly time-series of MOD13Q1 near-infrared band (EarthData)

red_landsat.glad.ard_{..}: Quarterly time-series of Landsat red band (Witjes et al., 2023)

red_mod13q1_{..}: Monthly time-series of MOD13Q1 red band (EarthData)

swir1_landsat.glad.ard_{..}: Quarterly time-series of Landsat short-wave infrared-1 band (Witjes et al., 2023)

swir2_landsat.glad.ard_{..}: Quarterly time-series of Landsat short-wave infrared-1 band (Witjes et al., 2023)

Temperature images:

lst_mod11a2.daytime_{..}: Monthly time-series of MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.daytime.{month}_{..}: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.daytime.trend_{..}: Deseasonalized monthly time-series (trend component of STL) for MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.daytime.trend.ols.alpha_{..}: Alpha coefficient / intercept (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.daytime.trend.ols.beta_{..}: Beta coefficient / trend (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.nighttime_{..}: Monthly time-series of MOD13Q1 night time land surface temperature (EarthData)

lst_mod11a2.nighttime.{month}_{..}: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (EarthData)

lst_mod11a2.nighttime.trend_{..}: Deseasonalized monthly time-series (trend component of STL) for MOD13Q1 night time land surface temperature (EarthData)

lst_mod11a2.nighttime.trend.ols.alpha_{..}: Alpha coefficient / intercept (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (EarthData)

lst_mod11a2.nighttime.trend.ols.beta_{..}: Beta coefficient / trend (derived by OLS) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (EarthData)

thermal_landsat.glad.ard_{..}: Quarterly time-series of Landsat thermal band (Witjes et al., 2023)

Climate layers:

accum.precipitation_chelsa.annual_{..}: Accumulated precipitation over the entire year according to CHELSA timeseries in mm of water (Karger et al., 2017)

accum.precipitation_chelsa.annual.3years.dif_{..}: 3-years difference considering the yearly accumulated precipitation according to CHELSA timeseries in mm of water (Karger et al., 2017)

accum.precipitation_chelsa.annual.log.csum_{..}: Cumulative sum, in logarithmic space, consdering the yearly accumulated precipitation according to CHELSA timeseries (Karger et al., 2017)

accum.precipitation_chelsa.montlhy_{..}: Accumulated precipitation for each month according to CHELSA timeseries in mm of water (Karger et al., 2017)

bioclim.var_chelsa.{variable_code}_{..}: Bioclimatic variables derived variables from the monthly mean, max, mean temperature, and mean precipitation values. For variable_code descriptions see chelsa-climate.org (Karger et al., 2017)

Accessibility & distance maps:

accessibility.to.ports_map.ox.{variable_code}_{..}: Time-required to access ports of different size according to Nelson et al., 2019

burned.area.distance_global.fire.atlas_{..}: Distance to burned areas mapped by Global Fire Atlas

cost.distance.to.coast_gedi.grass.gis_{..}: Cumulative cost of moving (derived by r.cost) to the coast

road.distance_osm.highways.high.density_{..}: Distance to high density of roads according to OpenStreetMap

road.distance_osm.highways.low.density_{..}: Distance to low density of roads according to OpenStreetMap

water.distance_glad.interanual.dynamic.classes_{..}: Distance to permanent / seasonal water bodies according to Pickens et al., 2020

Digital terrain model (DTM):

elev.lowestmode_gedi.eml_{..}: Mean estimate of the terrain elevation in dm filtered using SAGA GIS Gaussian filter (Witjes et al., 2023)

slope.percent_gedi.eml_{..}: Mean slope in % derived from terrain elevation ([Witjes et al., 2023]

Other existing maps:

pop.count_ghs.jrc_{..}: Annual time-series of population count in number of people mapped by Schiavina et al., 2023

snow.duration_global.snowpack_{..}: Annual duration of snow occurrence mapped by Global SnowPack

Files

train.csv: Training set with 42,237 rows and 420 columns, including sample id (sample_id - index column), land cover code (land_cover), land cover label (land_cover_label), reference year (year) and 416 features / covariates

test.csv: Test set with 42,271 rows and 418 columns, including sample id (sample_id - index column), reference year (year) and 416 features / covariates

sample_submission.csv: a sample submission file with 42,271 rows and 2 columns, including sample id (sample_id - index column) and predicted land cover code (land_cover)

Data innovations happen daily: the semantic web, the cloud, visualization, mapping, sensors, spatial data infrastructures, etc. This portion of the Training Day will focus on recent access to public data initiatives in Canada with an emphasis on open government and open data. In this session participants will be introduced to data and participatory democracy, open data definitions and examples of good government policy. In addition, we will look at what some community groups are doing, the leadership in Canada’s big cities and the Province of BC by administrations and citizens. This will include licenses, open data initiatives, hackfests, hackathons, applications, challenges and opportunities. It is hoped that this overview will provide participants with insight about what is new in the Canadian access to public data world.

This dataset was created using LeRobot.

  Dataset Structure

meta/info.json: { "codebase_version": "v2.1", "robot_type": "so101_follower", "total_episodes": 71, "total_frames": 35438, "total_tasks": 1, "total_videos": 142, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:71"}, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/LeRobot-worldwide-hackathon/282_Tartu_A-sponge4.