AI + Humans: How Artificial Intelligence scales Human Expertise for Detecting Changes to the Earth

This post details how Orbital Insight’s partnerships, artificial intelligence expertise, software and systems engineering allows us to reliably detect semantic changes to the Earth’s surface. We will go through a generalized overview, as well as a specific case study to demonstrate how we can enable both AI and human analysts to each do what they are best at, and iterate towards a globally scalable solution.

Overview

OSM road networks in Cambodia

Figure 1: An area in rural Cambodia where we see the value of pairing current wide-area imagery with scalable artificial intelligence. Left-to-Right: OSM road networks (cyan, displayed over older stock imagery); Google Street Maps; and our newly derived road networks (red, derived directly from current imagery, displayed over older stock imagery).

Commercial Mapping: Challenges

High resolution satellite imagery coverage of Japan

Figure 2: Outlines of nearly 1,400 high-resolution (<1m GSD) images taken over Japan’s mainland, across the entirety of Q4 2018 (October through December). Rendered over a Bing imagery hybrid visualization, and filtered to include images with up to 40% cloud cover.

Medium resolution satellite imagery coverage of Japan

Figure 3: Outlines of every medium-resolution (3–5m GSD) image taken over Japan’s mainland, within the same time period as Figure 1 (Q4 2018). There are over 124,000 total (with the same 40% threshold for provider cloud score).

High vs medium resolution coverage of Japan

Figure 4: A time series of daily detections across the entirety of the Japanese mainland, showing the scale of combined coverage (in square kilometers) covered between a medium resolution imagery provider and multiple high-resolution imagery providers; both are again filtered to include up to 40% cloud covered imagery.

Orbital Insight imagery partners

Figure 5: A sampling of different types of imagery available to Orbital Insight for computer vision algorithms. We can optimize between the relative availability and fidelity among them for both initial detection as well as tipping-and-cueing and validation.

Orbital Insight tiers of effort

Figure 6: Three Tiers of Effort, powered by Orbital Insight’s technologies and partnerships, enable substantial cost and performance improvements for updating commercial quality data. A generalized concept of operations is outlined on the left, while corresponding vignettes are on the right.

Case Study: Finding New Roads across Japan

Tier 1: Regions of Interest and Automated Land Use Analytics

Orbital Insight land use results

Figure 7: Single time period results from Orbital Insight’s land use algorithm, applied across an entire city scale, and based on medium-resolution imagery (Planet, 3–5m GSD).

 

Orbital Insight roads as linestrings

Figure 8: Single time period results from Orbital Insight’s land use algorithm, showing specifically a roads-as-linestrings output class across a mixed urban/unpopulated area and challenging arid/desert conditions. These results are also based on medium-resolution imagery (Planet, 3–5m GSD), visualized over generic/older basemap imagery.

Tier 2: Efficient Validation of Changes Detected

Detected road changes in Airbus SPOT imagery

Figure 9: Internally-developed software for validating detected road changes in Airbus SPOT imagery, providing multiple resources for validation, and capturing updated results in a structured manner. We help users compare before vs after images around one of our initial detections, letting them quickly validate AI detections using multiple imagery sources.

Orbital Insight AI validations

Orbital Insight AI validations

Figure 10: Results of software validation, again leveraging Airbus SPOT imagery to compare before vs after images around one of our initial detections; this lets the analyst quickly validate AI detections using multiple imagery sources.

Tier 3: Rigorous Road Data Curation and Updates

How much time and cost can GO save?

Traditional vs geospatial analysis cost analysis

Figure 11: Example of cost comparisons for commercial-grade road detection of all over Japan.

Conclusion


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