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Dataset 1: Change detection validation 2019
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This dataset contains web-based validations of changes detected by time series (2016  2019) analysis of Sentinel-2 satellite imagery.  Validation was conducted using two high resolution orthophotos from respectively 2016 and 2019 as reference data. Two tools have been used: Paysages web application and LACO-Wiki. Both tools used the same validation design: blind validation and the same options. For each detected change, contributors are asked to validate if there is a change and if it is the case then to choose a LU or LC class from a pre-defined list of classes. 

Contributor profiles include:
User 1 and User 2: First year students of the National Engineering in GIS School (ENSG). This group attended a brief training event on aerial photograph-interpretation comprising a 1-hour lecture and 2-hour training session on aerial photograph interpretation. This profile comprises two groups of 30 students each and corresponds to respectively User 1 and User 2.

User 3:  LULC experts working at the research department of IGN-France (LASTIG). This group includes four research experts in LULC data. These contributors were aware of the LandSense project and the change detection algorithm used within it.

User 4: Administrative staff from IGN-France. This group comprises two staff from IGN, non-experts in LULC data or geographic data in general.

User 5: LULC experts from the Urban Planning Agency in Toulouse. They have good knowledge of LULC classification, aerial photograph-interpretation and geographic data mapping, but few have knowledge of remote sensing.

User 6: First year students of ENSG who have not received training in aerial photograph-interpretation. This group consisted of a set of 15 students volunteers, who had begun their one-month curriculum in geographic sciences. They were motivated by gaining a better understanding of the processes and environmental applications related to LULC and the connection to the research world. These students did not have any specific training related to LULC, image analysis or aerial photograph-interpretation and were not experts in LULC.
User 7: Masters degree students in Geographical Information Science (GIS) at ENSG. The contribution was part of their curriculum and the data collected were used further in their practical work. These students had, as part of their curriculum, already studied remote sensing, spatial classification and spatial data validation before taking part in experience. Of the 19 students, some also had experience in aerial photograph-interpretation. However, these students are not experts in LULC data.

The dataset has the following characteristics:
	Time period of the change detection: 2016-2019.
	Time period of data collection: February 2019-December 2019
	Total number of contributors: 105
	Number of validated changes: 1048; each change was validated by between 1 to 6 contributors.
	Region of interest: Toulouse and surrounding areas

Associated files: 1- Change validation locations.png, 1-Change validation 2019  Attributes.csv, 1-Change validation 2019.csv, 1-Change validation 2019.geoJSON


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Dataset 2: Land use classification 2019
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The aim of this data collection campaign was to improve the LU classification of authoritative LULC data (OCS-GE 2016 ©IGN) for built-up area. Using the Paysages web platform, contributors are asked to choose a land use value among a list of pre-defined values for each location.  

The dataset has the following characteristics:
	Time period of data collection: August 2019
	Types of contributors: Surveyors from the production department of IGN
	Total number of contributors: 5
	Total number of observations: 2711
	Data specifications of the OCS-GE ©IGN: https://geoservices.ign.fr/ressources_documentaires/Espace_documentaire/BASES_VECTORIELLES/OCS_GE/DC_OCS_GE_1-1.pdf

Associated files: 2- LU classification points.png, 2-LU classification 2019  Attributes.csv, 2-LU classification 2019.csv, 2-LU classification 2019.geoJSON

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Dataset 3: In-situ validation 2018
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The aim of this data collection campaign was to collect in-situ (ground-based) information, using the Paysages mobile application, to update authoritative LULC data. Contributors visit pre-determined locations, take photographs, of the point location and in the four cardinal directions away from the point and answer a few questions with respect with the task. Two tasks were defined:  
	Classify the point by choosing a LU class between three classes: industrial (US2), commercial (US3) or residential (US5). 
	Validate changes detected by the LandSense Change Detection Service: for each new detected change, the contributor was requested to validate the change and choose a LU and LC class from a pre-defined list of classes. 


The dataset has the following characteristics  
	Time period of data collection: June 2018  October 2018
	Types of contributors: students from the School of Agricultural and Life Sciences and citizens
	Total number of contributors: 26
	Total number of observations: 281
	Total number of photos: 421 
	Region of interest: Toulouse and surrounding areas

Associated files: 3- Insitu locations.png, 3- Insitu validation 2018  Attributes.csv, 3- Insitu validation 2018.csv, 3- Insitu validation 2018.geoJSON

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Keywords:  land cover, land use, authoritative data, crowdsourcing, citizen science, change detection, Sentinel-2, in-situ, urban, Toulouse

All three datasets are licensed under a Creative Commons Attribution 4.0 International. 
It is attributed to the LandSense Citizen Observatory, IGN-France, GeoVille and the International Institute for Applied Systems Analysis.


This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement no 689812.
