Google earth engine is an open source cloud based computing platform that is designed to process large scale datasets (Prasai et al., 2021; Inman & Lyons, 2020; Gorelick et al., 2017; Midekisa et al., 2017). Using google earth engine, users can perform analyses in platform of Google cloud (Prasai, 2022). Users do not require the latest computers or software to work on big data (Phan et al., 2020; Thieme et al., 2020). It enables users to make quick visualization, analyses and research using the satellite images of our planet (Thapa & Prasai, 2022). Owing to its capacity and features, it can be useful to wide range of applications-the most prominent being vegetation mapping and monitoring (Venkatappa et al., 2019, Wang et al., 2015), disaster management (Xia et al., 2019), earth sciences related studies and many others (Prasai et al., 2021; Gorelick et.al; 2017).
We have developed a web tool based on google earth engine to encourage and assist the researchers from non-programming background to use google earth engine. This application is freely available and can be accessed at https://mapcoordinates.info/. This tool extracts the datasets from the google earth engine database. It uses Python API to interact with google earth engine database. In the current version, this application has some interesting features like data filter, generates time series plots, time series records and metadata in .csv format. Users can download the time series records of any location, select the satellite sensor, choose the algorithm to process the datasets, filter the cloud cover, scale factor. This application also visualizes the time slider feature for the location selected on the map.
1.1. Conceptual Framework
https://mapcoordinates.info/ has a web-client as the front-end and GEE as computing back-ends. The front end is the graphical user interface (GUI) web client where the users can specify the algorithms, date range, filter the datasets and send requests to run the analyses (Fig. 1). We used Ipywidgets a python based library, HTML and CSS to design GUI/front end of this application. Since all storages and computing operations are made on the cloud (GEE), the web-client can be accessed from any browser supporting device such as mobile, laptop or desktop computing devices. However, the framework is developed mainly with PC environments in mind and thus it is not optimized for mobile applications. The back end uses GEE to access satellite data, conduct the analyses and use Google’s cloud computing capabilities. We used Python API to interact with GEE backend. Current version of this tool has 3 algorithms (NDVI/NDCI), 2 BDA, Turbidity Index). We can select the location using polygon icon present on the left corner of the tool. It provides time slider to detect the changes on the map. Users can filter the datasets based on scale, clouds cover, dates. They get the time series datasets and metadata in .csv format and plots after passing request through submit button present on the GUI.
Video link: https://www.youtube.com/watch?v=jgkYn6oyucY