52 Open Source Earth Software Projects
Free and open source earth code projects including engines, APIs, generators, and tools.
Webworldwind 693 ⭐
Worldwindjava 590 ⭐
The NASA WorldWind Java SDK (WWJ) is for building cross-platform 3D geospatial desktop applications in Java.
Worldwindandroid 222 ⭐
The NASA WorldWind Java SDK for Android (WWA) includes the library, examples and tutorials for building 3D virtual globe applications for phones and tablets.
Esipfed Sweet 79 ⭐
Official repository for Semantic Web for Earth and Environmental Terminology (SWEET) Ontologies
Earthlab.github.io 71 ⭐
A site dedicated to tutorials, course and other learning materials and resources developed by the Earth Lab team
Worldwindearth Explorer 64 ⭐
WorldWindExplorer: A 3D virtual globe geo-browser app framework based on WorldWindJS, Bootstrap and KnockoutJS. Includes 3D globe and 2D map projections, imagery, terrain, markers, plus solar and celestial data.
Himawari 8 Chrome 53 ⭐
🛰 Experience the latest image from the Himawari, GOES, Meteosat, and DSCOVR satellites
Worldweather 45 ⭐
The largest three-dimensional web-based interactive browser of satellite, weather, climate, and other time-aware geospatial data on the web, built upon NASA's revolutionary WorldWind technology.
Google Globe Trends 21 ⭐
Create beautiful and interactive Google Trends globe visualizations with ease
Worldwind React App 12 ⭐
Geo-browser web app using the Web WorldWind virtual globe SDK from NASA and ESA with React and Bootstrap 4
Rsgislib 45 ⭐
Remote Sensing and GIS Software Library; python module tools for processing spatial data.
Arcsi 15 ⭐
Software to automate the production of optical analysis ready data (ARD) from Landsat, Sentinel-2 and others.
Oneworldsdkforunity 29 ⭐
A whole-earth visualization in Unity with support for WGS-84 coordinates and multiple levels of detail.
Landsat8image 33 ⭐
A simple python script that, given a location and a date, uses the Nasa Earth API to show a photo taken by the Landsat 8 satellite. The script must be executed on the command-line.
Earth Viewer 14 ⭐
Android application on Google Play. Animated planet Earth with live weather and satellite data.
Predicting Paid Amount For Claims Data 12 ⭐
Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.