Ideas for future: Continuous road health monitoring from space
Ideas for future: Continuous road health monitoring from space
Highlights from our ESA Act In Space experience
We recently participated in Act In Space 2022, an international hackathon for space under the problem title The Power of Earth Observation & AI. This problem required us to “use Artificial Intelligence to develop new applications from Earth Observation data” using a GNSS-based concept. Given that we have been working on onboard processing for satellites, a field which is abuzz with activity recently, we decided to explore and apply it to a real-world use case. Here’s a summary of our proposed solution:
The map problem
Commonly used Navigation Tools (like Google Maps for route plan and GNSS for real-time location) use road network generated from dated Satellite, Mobile and Survey maps. And dated maps used for navigation miss the following dynamic road health parameters: recent road damage, snow cover or mud cover (or landslide), blockages due to repairs, flooded road segments, development of new routes and fog or smog affecting road visibility in a local area. Clearly, it leads to consequences of traffic congestion, accidents, delay in the Average Transit Time for general people, businesses and even emergency medical ambulances.
The loss due to non-availability of maps incorporating dynamic road health is non-trivial. Every driver loses 29 hours a year due to bad maps, which means that just for the United States, a loss of $ 9B (assuming a really conservative 10 mn travellers, 32$/hr hourly wage). Globally, the above number maybe a much, much larger value.
Proposed solution
It seems to make a lot of sense to have a future where dynamic maps (changing features with time) are generated using Onboard Processing of state of the art Road Surface Classification and Segmentation Models on the latest satellite imagery of the user locality. The proposed solution supports the precise localization capability of GNSS with near real-time maps meeting Optimal Route Planning requirements.
Alright! Now, we know that GNSS receiver chips provide precise localization (< 4.5 m) at a high frequency enabling convenient navigation for end users and continuous information about position and velocity to navigate to the desired destination. GNSS-based localization is superior to alternative methods such as inertial navigation as they are prone to drift errors. Also, the location of the user obtained from the receiver enables continuous generation of the optimal route to the destination from the dynamic maps. And onto the onboard processing of satellite imagery to generate the dynamics maps. Optical High Resolution Multispectral Satellite Imagery from satellites like Plaiedes can be used to build state-of-the-art Road Surface Classification and Segmentation Models (Deep Learning models) which can generate road health insights. These small-sized insights can be downlinked to ground servers to update the Road network edge weights based on dynamic Road Surface Classification and Segmentation insights from satellites. This allows us to factor dynamic road health into every Navigation solution to go from point A to B.
A use case for this application for flooded roads is illustrated below:
Dynamic Road Health Maps from Smart Earth Observation can save dozens of hours travel time for 100s of millions of users. This solution does not exist in India and the amount of man-hours saved could significantly contribute to the Indian economy: Assume 30 million workforce (average hourly rate in India INR 96 source) using navigation solution in India saving 20 hours of travel time a year. This translates to saving INR 5760 crores each year. Technology innovation involves building state of the art Road Classification and Segmentation Models for Onboard Processing of Satellite Imagery which is clearly feasible.
Insight generation using remotely sensed satellite imagery is proven to have a short turn around time and low price-point. Adithya et al [1] A road extraction model with an F1 score of 0.947 is presented and tested on varied satellite imagery. V.Yerram et al [2] A road surface classification model with an accuracy of 80% for roads in USA is demonstrated in literature. E.Brewer et al [3] Onboard AI models for cloud detection have been tested by ESA -Giuffrida et al. [4] An inflight accuracy of 96% has been reported. Similar to this application a road surface classification and segmentation model can be deployed onboard.
Market
The Earth Observation Market is Booming: With planned Earth Observing Satellites, the revisit duration over an area will be reduced to between minutes to hours. 8 million Autonomous cars projected to be on the road by 2025. Is it a coincidence that these two sectors will talk to each other more? Guess not.
The demand stems from the complex navigation problems which affect us all and can save at least $ 9B (in US and higher globally) of lost value as highlighted earlier. The supply from the GNSS and Earth Observation market make it a feasible opportunity with Imagery costs projected to go significantly down with the onset of Onboard Processing.
Winding up
With the growing presence of high-resolution optical satellites employing onboard processing, use cases like building dynamic road health maps emerge as practical solutions to get feature-rich, near-realtime maps globally. Road Classification and Segmentation Models to run on multispectral imagery can are shown to have feasibility and will improve in accuracy. With GNSS-based precise localization, navigation can be made much more efficient and realistic than it is today. While it was fun to put forth these ideas, we at SkyServe intend to demonstrate these and similar ideas seriously through technology demonstrators in the coming years.
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References
[1] Kothandhapani, Adithya & Vatsal, Vishesh. (2020). Methods to Leverage Onboard Autonomy in Remote Sensing.
[2] Extraction and Calculation of Roadway Area from Satellite Images Using Improved Deep Learning Model and Post-Processing, V.Yerram et al
[3] Predicting road quality using high resolution satellite imagery: A transfer learning approach, E.Brewer et al
[4] G. Giuffrida et al., "The Φ-Sat-1 Mission: The First On-Board Deep Neural Network Demonstrator for Satellite Earth Observation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art no. 5517414, doi: 10.1109/TGRS.2021.3125567.