rdl-standard

Risk standard

Standardizes data models for disaster and climate risk assessments

The Risk Data Library Standard (RDLS) is an open data standard to make it easier to work with disaster and climate risk data. It provides a common description of the data used and produced in risk assessments, including hazard, exposure, vulnerability, and modelled loss, or impact, data.

GitHub

16 stars
14 watching
1 forks
Language: Python
last commit: 2 months ago
Linked from 1 awesome list

climate-datadisaster-risk-managementhazard-assessmentjsonopendatarisk-assessmentstandard

Backlinks from these awesome lists:

Related projects:

Repository Description Stars
gfdrr/ccdr-tools A collection of scripts and tools to support subnational disaster risk analysis using global datasets 16
ghislainv/riskmapjnr A tool to calculate risk of deforestation and forest degradation using the JNR methodology 24
deltares-research/floodadapt An open-source decision-support tool that enables users to rapidly model and evaluate flood risks and adaptation options using physics-based compound flood modeling. 5
gfdrr/thinkhazard An application that provides hazard level classification and risk management advice for disaster projects worldwide. 33
rdflib/geosparql-dggs An implementation of GeoSPARQL's Simple Features functions for DGGS geometries 9
nismod/open-gira Analyzes global environmental risks to infrastructure networks using open data 13
yuanchao-xu/gfer Researches and analyzes green finance and environmental risk data using R 8
rdflib/owl-rl A Python library for expanding RDF graphs according to the OWL2 RL Profile using mechanical forward chaining 144
brry/rdwd A tool for retrieving climate data from the German Weather Service 72
drlivingston/kr Provides a unified interface for RDF and SPARQL APIs including Jena and Sesame. 56
doi-usgs/dataretrieval A package to simplify loading USGS hydrologic data into the R environment using web services. 263
conglu1997/v-d4rl Provides pre-built datasets and code for offline reinforcement learning from visual observations using deep learning algorithms 95
anuzzolese/pyrml Engine for processing customized mapping rules from heterogeneous data structures to RDF data model 33
geoscienceaustralia/tcrm A statistical model for assessing wind hazard from tropical cyclones 83
noaa-gfdl/fms A software framework for building and running complex climate system models 94