The software of climate adaptation

Climate change manifests in our lives in two major ways. Firstly, we are seeing an accelerated increase in the environmental impacts and economic losses from physical climate risk. Secondly, we are observing the growing awareness of a climate emergency at the scientific (IPCC AR6) and geopolitical stage (COP26), likely to lead to new regulatory policy.

We live at a pivotal point in history. Over the past decades, we have seen many domains, such as web, mobile and payments, disrupted and redefined by software. Now, more than ever, we are ready for a software-driven, machine learning-powered approach towards climate adaptation. Here are the reasons why I feel the coming decade is one where we will see the emergence of software for climate adaptation:

• Data abundance: Climate data is inherently geospatial. By that, I mean there is spatial variability – different regions are affected differently by different manifestations of climate change. Between near real-time environmental sensing using satellites, ground-based sensor observations and sophisticated climate modeling, the challenges in climate adaptation are those of “big geospatial data” (similar to big data in commerce in the 2010s). 

The ability to perform cloud native aggregations, harmonization and asset-level query of climate risk manifestation (physical climate risk), climate risk attribution (emissions monitoring) and climate mitigation (strategic investments in reforestation, carbon sequestration and resilience measures) forms the cornerstone of the new software powered ecosystem for climate adaptation.

• Rethinking machine learning for climate: Large volumes of earth observation and climate projection data can be made actionable when  Machine Learning (ML) powered approaches are applied. ML models operating in the software of climate adaptation will need to learn and serve inference on spatiotemporal data. These are inherently different from pure play computer vision and statistical learning techniques. They require deeper rethinking of spatial attributes (spatial feature engineering) and multivariate inputs (temporal harmonization and analysis ready multispectral data). 

Unlike photorealistic imagery, analytics on satellite-derived environmental observations and geospatial climate projections cannot easily leverage transfer learning from similar analytics trained on other datasets. Super resolution and feature classification would require creating new training datasets based on historic observations that serve inference on forward looking climate projection datasets. One such deep learning based model approach is FireSRnet, for super resolving (or downscaling) scenario-driven wildfire projections.

• Approaching conscious capitalism: The last 200 years following the industrial revolution have seen humans use the environment with limited concern beyond pure capitalism. As a society, we are now realizing that there is a cost to the environment through our actions, which manifests as climate change and an opportunity for re-alignment. We will need to pay back that cost in the long-term as climate-related damages from acute hazards (fires, flood, cyclones) and migration due to chronic hazards (sea level rise, heatwaves, droughts).

Through finding new solutions to address the climate crisis, we not only reduce the existential risks facing our species, but also create significant opportunities to move the economy towards a more conscious form of capitalism. This new normal also creates significant positive externalities and financial opportunities for the companies and leaders who are able to drive this change.

As we distill adaptation and mitigation measures, the software of climate adaptation will need to be inherently cloud native and API driven, powered by and powering businesses that are willing and ready to adapt. Such software has the immense potential to drive conscious capitalism, serving businesses with a mantra of elevating humanity and human action through business. 

That journey begins with knowing WHY you, WHY your software team and WHY your company exists. For an organization, answering these questions are key additions to realizing revenues and making a profit. Conscious capitalism can also serve as a north star for an organizational model as businesses collaborate and partner towards creating new software products and capabilities in the year to come. 

From 2016 to 2020, I led the development of Planet Analytics, where my team served data-driven insights from petabytes of satellite imagery across a range of verticals. We had the chance to think through emergent models for the next generation of geospatial data and software, something Josh Gilbert and I ambitiously called ‘Geospatial 2.0’. 

The above themes are what inspired Josh and I to create Sust Global, a new venture driven by the mission to create the objective source of truth for climate data, and to define next generation software capabilities to serve climate adaptation. Satellite-derived data from earth observation, in combination with frontier climate models, can enable effective and usable indicators of climate-related risks. By integrating and transforming these large scale geospatial datasets, we are developing the foundational stack of geospatial software for large scale, ML driven inference on asset level exposure to climate-related risk and asset level attribution of industrial emissions. 

We believe that driving awareness and developing a data-driven understanding of climate-related risks to businesses will place us, as a society, on a path towards climate adaptation. I talk about all these topics in greater depth in this podcast at Software Engineering Daily with Kyle Polich. 

‎Software Engineering Daily: The Software of Climate Adaptation with Gopal Erinjippurath on Apple…

‎Climate modeling is increasingly important as supply chains, emergency management, and dozens of other efforts need to…podcasts.apple.com

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