Delivers data through IBM’s new Environmental Intelligence API

IBM’s beta release Environmental intelligence (EI) platform offers application developers and data scientists curated environmental insights powered by AI with access to open-source geospatial and satellite data.

This cloud-based platform provides a variety of geospatial, weather and climate APIs, enabling users to build applications that address sustainability, climate change and regulatory needs.

IBM software aims to help businesses manage the financial risks associated with climate disruption Both the public and private sectors face climate-related challenges, including operational disruptions, resource losses and supply chain vulnerabilities. IBM Environmental Intelligence provides geospatial data, advanced models and AI insights to help organizations solve these complex problems.

“Environmental intelligence is a new product, but some of its remnants date back a few years. We have a very broad IBM research community of sustainability and climate scientists who are investing in and working with specific industry-type issues, climate-type issues that are really forward-thinking,” said David Blanch, Director of Product Management, ESG, and Environmental at IBM. Intelligence, says TechNewsWorld.

Versatile API Suite Powers Environmental Insights

IBM’s EI solution offers a suite of APIs to help developers and data scientists collect and analyze Earth’s surface data for predictive insights and proactive decision making. It simplifies the process of working with public satellite data by processing, normalizing and organizing it into geospatial layers, making it ready for analysis and visualization.

Satellite imagery highlights biomass data to support environmental monitoring and sustainability initiatives.

APIs and a Python software development kit (SDK) provide access to high-resolution imagery, global weather data, and other valuable datasets to gain insights for rapid action. The EI platform includes a foundation model developed with NASA for pre-processing and cleaning geospatial data.

Other key features include seamless access to environmental and geospatial datasets through easy-to-use APIs for real-time analysis. Geospatial-temporal search uses an advanced insight engine to analyze geospatial and temporal data and improve application performance.

IBM’s Environmental Intelligence Platform includes data layers, predictive models and insights to support decision-making and sustainability efforts.

On-demand historical weather data provides access to past weather data to inform predictive models and optimize operations across sectors. Advanced Data Query allows users to perform complex queries on various environmental datasets at customizable spatial and temporal resolutions.

APIs automate greenhouse gas (GHG) emission calculations for internal and supply chain emissions to track and manage carbon footprints.

The beta platform is free. IBM will consider future pricing and packaging decisions pending market feedback.

Market trends show new uses for climate data

IBM targets a specific audience of technical makers, developers and data scientist communities due to observed trends in the market. According to Blanche, these target users are not necessarily all climate scientists or meteorologists who benefit from accessing innovative data, putting it behind an API and applying it to embeddable use cases in their existing applications.

Trends that involve how climate change is affecting business operations across different verticals. Research indicates that climate-related disasters are causing significant damage at a macro level Some datasets in environmental intelligence fall into a few buckets.

One dataset is Open-Source Satellite Imagery, which offers high-resolution visuals captured by European satellites orbiting the globe every five days. This image supports a wide range of applications from environmental monitoring to regulatory compliance.

“An organization can access those raw images themselves but will face a lot of challenges with removing the pre-processing cloud,” Blanch notes as a key benefit of using the EI platform instead.

Another dataset includes meteorological information such as historical records, real-time updates and short-term weather forecasts. Blanch noted that climate projections should be added to those scenarios for more long-term needs involving climate planning.

Thinking in terms of specific business examples, how can you build more resilience within your organization to natural hazards such as floods and wildfires? How do you conduct disaster risk planning and risk assessment? How do you calculate the carbon emissions of your business?

Visualizing wildfire risk across regions using IBM’s environmental intelligence platform for disaster planning and resilience.

“If you think about how companies are implementing sustainability, they have an industrial asset and a carbon equivalent methane link in today’s workflows. A work order can be issued so that someone can do some maintenance for these different operational groups who are not traditional sustainability-minded people,” Blanche suggested.

Sustainable solutions for various industries

According to Blanch, IBM wants to expand beyond traditional use to benefit potential applications in other industries. He cites use cases that already show EI success in agriculture, such as crop monitoring, yield forecasting and precision farming.

For example, easy access to satellite data is helping companies comply with new environmental regulations from governments. The European Union’s deforestation regulations are a case in point.

This requires companies to review their supply chains and verify that they are not working with suppliers that violate these regulatory requirements that involve extensive land use change.

Another example involves how IBM’s EI technology can affect animal growth and nutrient adjustment based on environmental conditions. Some groups have developed a predictive modeling system using weather data to predict mycotoxin levels in their crops. They were able to create a forecasting model using environmental and weather data from the platform to improve their business operations.

Temperature distribution maps support predictive modeling for agricultural and climate-related decision-making.

“The potential for third-party, remote imaging is tremendous. So you don’t need to be a meteorologist or climatologist, but you can access a pre-processed, curated data set with easy tutorials, samples, and guides to embed into your applications, models, or workflows. ,” Blanch said of IBM’s EI software goals.

OpenStreetMap images in this article were provided to TechNewsWorld courtesy of IBM