New Space Intelligence provides satellite data pipeline services that streamline the selection, analysis, and delivery of satellite imagery tailored to specific business needs. By automating the process of matching user requirements with available satellite data, the company enhances accessibility and reduces the technical burden associated with satellite data utilization.
Funding
$210K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Businesses often face challenges in efficiently accessing and interpreting satellite imagery for timely, data-driven decisions. Selecting the appropriate satellite data, ensuring its availability, and extracting actionable insights from complex imagery requires specialized expertise and can be time-consuming and costly.
Solution
New Space Intelligence (NSI) offers satellite data pipeline services that streamline the process of acquiring, analyzing, and delivering tailored satellite imagery. NSI automates satellite selection based on user requirements, matches needs with available data, and provides analysis-ready products. By handling the complexities of satellite data processing, NSI enables businesses to easily integrate satellite-derived insights into their workflows, optimizing costs and reducing the technical burden.
Target Audience
NSI's primary customers include businesses and government organizations across various sectors, including agriculture, infrastructure, disaster management, and environmental monitoring, who require timely and accurate geospatial insights.
Features
- Automated satellite selection based on user-defined criteria, considering factors like resolution, temporal frequency, and sensor type.
- Change detection services using optical and SAR imagery, employing machine learning and computer vision techniques.
- Normalized Difference Vegetation Index (NDVI) generation for agriculture and environmental monitoring.
- Land cover classification using machine learning to identify urban areas, water bodies, agriculture, bare land, and forests.
- Crop pattern change detection to monitor dynamic phenology at the field level.
- Ground deformation monitoring using Persistent Scatterer Interferometry (PSInSAR) techniques.
- Analysis-ready products including statistical analysis, machine learning models, time series analysis, clustering, optimization, and big data analysis.