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Description
Google Earth Engine serves as a cloud-centric platform designed for the scientific examination and visualization of geospatial data, granting users access to an extensive public archive containing over 90 petabytes of analysis-ready satellite imagery alongside more than 1,000 carefully curated geospatial datasets. This rich collection boasts over five decades of historical imagery that is refreshed daily, with pixel resolutions reaching as fine as one meter, showcasing datasets from sources such as Landsat, MODIS, Sentinel, and the National Agriculture Imagery Program (NAIP). Through its web-based JavaScript Code Editor and Python API, Earth Engine empowers users to perform analyses on Earth observation data while employing machine learning techniques, thereby enabling the creation of sophisticated geospatial workflows. The platform's seamless integration with Google Cloud facilitates large-scale parallel processing, allowing for thorough analyses and efficient visualization of Earth data. Furthermore, Earth Engine's compatibility with BigQuery enhances its capabilities, making it a versatile tool for users in various fields. This unique combination of features positions Google Earth Engine as an essential resource for researchers and professionals working with geospatial information.
Description
From significant hurricanes to damaging hail storms, Respond® predictive analytics enables you to effectively manage risks and optimize resources associated with severe weather events. By leveraging insights derived from real-time weather data feeds, Respond enhances your operational performance and aids in informed decision-making regarding weather-related risk assessments, preparation for extreme weather situations, loss prevention strategies, and disaster recovery efforts. The analysis provided by Respond, when superimposed on your existing policies in force (PIF), allows for a visualization of policyholder locations and coverage amounts that are likely to be affected by hazardous hail or other threats. This capability empowers you to deploy the right adjuster expertise for properties that are located within, adjacent to, or outside the affected storm zone. Furthermore, it streamlines the claims process, reduces cycle times, and boosts policyholder contentment. By improving reinsurance treaty recovery processes and accurately predicting claims volumes for natural hazard incidents, Respond not only aids in visualizing the impact of destructive weather but also facilitates a deeper understanding of storm intensity and its potential effects. Additionally, this comprehensive approach positions you to mitigate losses more effectively while enhancing overall operational resilience amidst unpredictable weather challenges.
API Access
Has API
API Access
Has API
Integrations
GeoPandas
Google Cloud Analytics Hub
Google Cloud BigQuery
Google Cloud Platform
JavaScript
Python
WeatherNext
Integrations
GeoPandas
Google Cloud Analytics Hub
Google Cloud BigQuery
Google Cloud Platform
JavaScript
Python
WeatherNext
Pricing Details
$500 per month
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Founded
1998
Country
United States
Website
cloud.google.com/earth-engine
Vendor Details
Company Name
Xactware
Founded
1986
Country
United States
Website
www.xactware.com/en-us/solutions/weather-analytics/respond/overview/
Product Features
GIS
3D Imagery
Census Data Integration
Color Coding
Geocoding
Image Exporting
Image Management
Internet Mapping
Interoperability
Labeling
Map Creation
Map Sharing
Near-Matching
Reverse Geocoding
Spatial Analysis