PyGMTSAR (Python InSAR): Powerful and Accessible Satellite Interferometry
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Updated
May 27, 2024 - Jupyter Notebook
PyGMTSAR (Python InSAR): Powerful and Accessible Satellite Interferometry
A Django application to archive real-time earthquake notifications from the USGS's Advanced National Seismic System
the natural disaster algerian api
Detects emergency situations using an React-Native app and Firebase
[🥉 3rd place] Detect damaged building by natural disasters and get a brief information about it.
Messiah: The Mighty Son Of God Is Here To Help You Through Times Of Calamity
Exploring deaths by natural disasters, and possible relations to climate change.
Prediction of market premiums for property damage and business interruption insurance products. Added natural hazard data and stacked 3 best models as the final model.
Alert users of impending Disasters near them, provide them with relief info.
Repository for model of models development.
A visualisation of natural disasters, pandemics, and extreme weather events in python over periods of time and locations.
Fire Information for Resource Management System - Tool
A webapp that displays natural disasters on an interactive map. Built with Mapbox and LeafletJS.
Modular structure that feeds on instant data, warns about natural disasters and conveys what needs to be done
[WIP] Get smart insights, alerts & warnings about predicted natural disasters and take precautions before they arrive to keep family, friends & yourself safe.
Jupyter Notebook for representing the data of world-natural-disaster events
Student research paper on precaution and prevention of natural disasters
Data visualization of death toll by natural disasters - written in Python3 using tkinter (GUI) and matplotlib (Visualization) .
The objective of the project is to predict whether a particular tweet, of which the text (occasionally the keyword and the location as well) is provided, indicates a real disaster or not. We use various NLP techniques and classification models for this purpose and objectively compare these models by means of appropriate evaluation metric.
Add a description, image, and links to the natural-disasters topic page so that developers can more easily learn about it.
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