ReaLSAT, a global dataset of reservoir and lake surface area variations

Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset provides an unprecedented reconstruction of surface area variations of lakes and reservoirs at a global scale using Earth Observation (EO) data and novel machine learning techniques. The dataset provides monthly scale surface area variations (1984 to 2015) of 683734 water bodies below 50°N and sizes between 0.1 to 100 square kilometers.

 The dataset contains the following files:

1) ReaLSAT.zip: A shapefile that contains the reference shape of waterbodies in the dataset.

2) monthly_timeseries.zip: contains one CSV file for each water body. The CSV file provides monthly surface area variation values. The CSV files are stored in a subfolder corresponding to each 10 degree by 10 degree cell. For example, monthly_timeseries_60_-50 folders contain CSV files of lakes that lie between 60 E and 70 E longitude, and 50S and 40 S. 

3) monthly_shapes_<bottom_left_lon>_<bottom_left_lat>.zip: contains shapefile for each water body that lie within the 10 degree by 10 degree cell. The shapefile provides monthly shapes together with their time and area attributes. 

4) ReaLSAT.html: a readme python notebook that provides information about reading and visualizing the dataset. The notebook also contains the code to download the data to reduce the overhead of downloading each file manually.

5) evaluation_data.zip: contains the random subsets of the dataset used for evaluation. The zip file contains a README file that describes the evaluation data.

6) generate_realsat_timeseries.ipynb: a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody.

Please refer to the following papers to learn more about the processing pipeline used to create ReaLSAT dataset:

[1] Khandelwal, Ankush, Rahul Ghosh, Zhihao Wei, Huangying Kuang, Hilary Dugan, Paul Hanson, Anuj Karpatne, and Vipin Kumar. "ReaLSAT: A new Reservoir and Lake Surface Area Timeseries Dataset created using machine learning and satellite imagery." (2020).

[2] Khandelwal, Ankush. "ORBIT (Ordering Based Information Transfer): A Physics Guided Machine Learning Framework to Monitor the Dynamics of Water Bodies at a Global Scale." (2019).

Version Updates

Version 1.3: fixed visualization related bug in generate_realsat_timeseries.ipynb

Version 1.2: added a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody in ReaLSAT database.