Foundational Layer of smartness

Building upon Part-1: "City to Smart City: 'Smartness Proportion' with Satellite Intelligence", let's explore some activities undertaken by smart city development bodies to demonstrate the utility of satellite edge computing. Before we proceed, let us keep in mind that cities have the autonomy to chart their unique paths towards "smartness", without being rushed or pressured to adopt a specific technology or even the extent of its application.

In his publication (2016), “Smart City Implementation Framework for Developing Countries: The Case of Egypt”, Dr.Karim Hamza suggested the following framework.

Figure 1 illustrates the "Smart City Structure", highlighting the top priorities such as preserving land, water, and forest resources, along with establishing robust infrastructure for sewage systems, water supply and transportation. Maintaining up-to-date digital twins of these features is critical for every city in developing nations, as they aspire to integrate "Smartness" into their urban landscapes, reflecting a holistic approach to urban development.

Without this groundwork, cities may encounter obstacles in subsequent stages of smart city development. This is where satellite edge computing can be a transformative solution, offering an alternate to traditional high-cost approaches.

Unlocking Smartness: Satellite Edge Computing Advantages

Satellite edge computing offers a host of advantages for cities aspiring to embrace smartness. It can seamlessly integrate with existing systems, thereby reducing upfront costs and overhauling legacy infrastructure. Even cities with limited digital infrastructure can benefit during early development stages, accelerating their smart city initiatives. Moreover, by providing actionable insights directly, satellite edge computing reduces the reliance on in-house expertise at the very beginning, enabling swift action and building trust with their citizens for delivering on public goods.

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In consonance with our focus on "City-led" approaches (recall the generations of smart cities outlined earlier), technology only serves as an enabler.

1. Understanding city dynamics – Master Plan Compliance

The increased frequency of data collection, facilitated by satellite edge computing, simplifies the identification of illegal land use, a prevalent issue in many developing nations, through classification models onboarded onto satellites. This capability is crucial for city authorities as it allows them to address illegal land use at its beginning stages, supporting various aspects of urban governance. Additionally, frequent monitoring of land use dynamics enhances the efficiency of disaster management activities. Furthermore, supports proactive urban planning by tracking urban sprawl and ensuring compliance with land-use regulations.

2.Infrastructure Development and Monitoring

In a developing city, construction projects span across roads, buildings and bridges dispersed across the urban landscape. Monitoring these projects from an executive-level often requires extensive field visits, creating a significant dependency on reporting officials.

In a city-led approach, where councils directly oversee project progress, the need for frequent field visits must be kept to a minimum. This means that instead of relying heavily on physical inspections, city officials can leverage technologies like satellite-based construction activity monitoring. With this system integrated into the smart city's monitoring infrastructure, a significant portion of the monitoring work is accomplished without the necessity for on-site visits.

Once a model that is trained to meet the requirements is onboarded, the system delivers regular snapshots of project sites, on a daily or weekly basis. These snapshots are accessible through standard web-mapping frameworks, eliminating the necessity for specialized GIS software and reducing the burden of frequent field visits.

This feature not only ensures effective work implementation but also results in cost-effectiveness. Considering that most infrastructure projects stretch beyond planned timeline and budgets, and involve a multitude of departments across administration tenures, satellite edge computing can be specifically enabled to cater to these extended periods, ensuring continuous monitoring and oversight.

3. Encroachment detection

Encroachment detection, a critical aspect of urban land management, relies on routine field visit which may not be timely or effective. However, with satellite edge computing, encroachment attempts can be swiftly detected and alerted upon, preventing the unauthorized use of land parcels. This proactive approach ensures the timely conservation of valuable land resources as well as minimize lost revenue to the municipality. Using satellite images in encroachment detection is a high-value activity, widely used by metro cities like Mumbai. Additionally, in forested areas adjacent to urban zones, early warning systems can be established to monitor deforestation activities, safeguarding vital ecosystems.

For further insights into proactive property monitoring and deforestation tracking using satellite imagery and edge computing, explore our case studies on Monitoring property lines and Deforestation early warning.

4. Natural resources – Management

Satellite-based monitoring is a very powerful tool for natural resource management, tracking water quality and green cover health. Satellite edge computing can be used to provide timely alerts when water quality drops below safe limits, enabling proactive interventions. Additionally, vegetation indices computed continuously can assess green cover health. This integrated approach to natural resource management can enable proactive conservation efforts towards keeping cities sustainable and healthy.

Skyserve conducted a case study illustrating the density of Cyanobacteria, which is known for causing severe social and ecological consequences. These blooms incur significant socioeconomic and environmental costs, affecting various sectors including drinking water, fisheries and habitats. Continuous monitoring of such contamination enables early detection of adverse effects, facilitating timely intervention through appropriate channels.

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5. Public health

Landfills hold complex significance in urban areas, preventing waste accumulation and associated health risks. Continuous monitoring of landfills is essential, not only for managing environmental and social impacts but also to preserve the land resources they occupy.

Satellite image-centric solutions offer vital inputs here. Alongside traditional techniques like land use classification and NDVI-based studies, deep learning models are providing better accuracy nowadays. From the medium to very high-resolution, satellite imageries are being trained with those models.

These models not only monitor landfill sites but also detect illegal landfills within cities, where human access and visibility are limited, with minimal effort. Continuous monitoring of official and illegal landfills through satellite edge computing enables the assessment of their growth rates, the development of elimination plans, and the implementation of measures to prevent new pollution.

6. Diverse AI Models to Solve City Problems

High-resolution satellite images can collect property-level information, allowing for automated monitoring of approved properties and detection of modifications. Tax officers can leverage this technology to identify changes and impose relevant taxes, thereby bolstering the financial health of the city corporation. These capabilities extend to many more applications that can be tailored to address city-specific issues. For instance, detection of potholes and road quality monitoring enables timely repair by highway departments, while city-wide traffic monitoring using satellite images informs traffic management strategies.

Empowering Smartness in Developing Nations: Role of AI/ML Models

AI/ML is already playing a crucial role in our quest to increase the smartness quotient of cities, especially in developing countries. These models provide invaluable support by automatedly analysing satellite images and extracting actionable insights, empowering city authorities to make informed decisions with a smaller workforce.

While training these models requires initial effort, the long-term benefits align with the goal of continuously integrating smartness within existing digital infrastructures. Additionally, the adaptability of AI/ML models allows for seamless upgrades to satellite systems, facilitating ongoing smart city development.

The increase in diversity and access to satellite datasets has not only streamlined the process of training AI/ML models, but has also democratized access to remote sensing solutions. This has catalysed innovation in the field, with many researchers, start-ups and enterprises leveraging these datasets to contribute to smart city objectives

Closing the Global Smart City Gap

Satellite edge computing isn't exclusive to developing nations; it can be a priority-driven strategy. Even cities in developed nations, opting to reduce ground-based data collection, may transition towards utilizing satellite edge computing.

We can see that today's cities can adapt very quickly if technologies like satellite edge-computing are adopted, while being prepared to on-ramp infrastructure modifications at their own pace. This prepares cities for future growth, ensuring resilience and adaptability regardless of their current developmental stage. Ultimately, it is the citizens and their trust in their local bodies that will foster a more livable urban environment for the benefit of society.

Introduction:

As of March 2024, only 142 cities out of 47,868 were recognized in the IIMD's smart city index report for 2024, indicating a significant gap in smart city development globally. This discrepancy becomes even more pressing when considering that, according to the UN, over 68% of the world's population will reside in urban environments by 2050, with a substantial portion concentrated in developing countries. The presence of blue dot clusters on the map further emphasizes the need for smart city advancements to accommodate the burgeoning urban populations in these regions.

What Drives the Attention on Developing Countries?

In the historical narrative of smart city initiatives, developed nations have typically spearheaded the movement. This momentum has been fuelled by substantial investments in Information Communication Technology (ICT) and the established robust infrastructures to align to smart city requirements. Nonetheless, these advantages may not be readily available to developing countries, given their diverse socio-economic conditions. Additionally, the industry's current focus on mature economies widens the gap in technology adoption and outcomes between developed and developing nations, a discrepancy especially noticeable in their smart city initiatives.

The Good News: The Definition of "Smartness" is evolving!

Boyd Cohen's article (2015) signals a significant shift in smart city development, emphasizing a dynamic perception of "smartness." This evolving understanding prioritizes a balanced integration of technology and city governance, emphasizing citizen engagement over excessive technology adoption. In contrast to the earlier Smart City 1.0 model, Smart City 2.0 empowers local municipalities to strategically select technologies aligned with their unique contexts. This evolution holds promise for narrowing the gap between developed and developing nations as they navigate the challenges and opportunities of smart city implementation. 

Balancing Technology Adoption

While technology undoubtedly plays a pivotal role in smart city initiatives, the emphasis should not solely be on adopting technology for the sake of it. Instead, there's a growing recognition of the importance of making strategic choices in technology adoption based on specific needs and circumstances. 

Consider the landscape of smart city initiatives in various developing nations: Roughly 1% of Indian cities, 30% of China's cities, 17% of Russia's cities, and 25% of Indonesia's cities are actively planning, participating in, or achieving smart city initiatives.

Verge of Overload: Rethinking the Sensor Network Paradigm

Amidst the ever-expanding landscape of smart city technologies, can cities, especially those in developing nations, effectively harness the multitude of options available?

Before delving into the specifics of technology, it's imperative to ponder the following:

The "Network of sensors" integrated with the Internet of Things (IoT) stands as a cornerstone of smart city endeavours. Traditionally, these systems heavily rely on sensor networks for real-time data collection, encompassing aspects like traffic flow, air quality, and energy consumption. While this framework presents undeniable opportunities, the implementation of extensive IoT deployments encounters substantial hurdles in developing countries. Challenges stem from the dearth of reliable infrastructure and services, spanning electricity, internet connectivity, roads, and urban structures. Furthermore, the sheer magnitude of sensor deployment poses maintenance complexities and the looming threat of "sensory overload”, a phenomenon Forbes has highlighted in its discussions on smart city challenges.

Additionally, projections from Electronic Specifier indicated that by 2020, one trillion sensors could have been deployed, magnifying the challenges associated with overreliance on sensor networks.

Redefining Smartness

Amidst challenges such as sensory overload and the unique hurdles faced by developing nations, actual “smartness” can greatly enhance daily living. Redefined smartness entails prioritizing basic infrastructure development and leveraging existing digital frameworks to connect administrations with citizens. This approach aims to bring immediate relief to people's lives by addressing fundamental needs like roads and electricity, while maximizing the efficiency and sustainability of smart city initiatives. It involves evaluating new technologies based on criteria such as capital efficiency and suitability for city economics, emphasizing minimal data collection and effective use of existing digital frameworks such as e-governance portals.

Satellite Imagery for Developing Regions

In the context of optimizing data collection and leveraging e-governance portals, remote sensing and Geographic Information Systems (GIS) stand out as indispensable tools for advancing smart city initiatives in developing countries. These technologies have long played a crucial role in urban planning and management, offering valuable insights and facilitating the integration of IoT solutions.

Satellite imagery, a key product of remote sensing and geospatial analysis, serves a multitude of urban planning applications. Its wide coverage and high accuracy make it particularly influential, enabling modern analysis and insight generation. However, challenges such as cloud coverage render satellite imagery unusable at times, while purchasing complexities like minimum order requirements and unclear licensing terms hinder accessibility.

When satellite images are obtained at no cost, the challenge of data storage becomes more pronounced. At its core, as the resolution of the images increases, so does the image size, exacerbating the storage issue.

Both open-source and commercially procured satellite imagery face processing time constraints, leading to latency issues.

Limited budgets mean authorities may have to wait up to three days to gain insights from satellite images after processing, further highlighting the budget constraints!

Overcoming these challenges is essential to fully realize the benefits and allowing cities to unlock the transformative potential of satellite imagery.

Satellite Edge Computing – No longer futuristic

Satellite imagery, previously limited by inherent issues, is now undergoing a paradigm shift with satellite edge computing.

Satellite edge computing is no longer futuristic! Our ongoing missions are listed here. We have completed our first set of demonstrations processing data in space - and it's not just us, there will be more announcements coming in this field from other ventures as well!

Former NASA Administrator, Dan Goldin, recently extended his support to put more powerful computers in space to run AI in real-time. This is the start of an era in which trained AI/ML models can be uploaded to a relevant satellite, which can swiftly applying corrections to captured images and identify specific areas of interest with precision and the inference from the model would be then be downloaded for the user. The game-changing part is the speed with which these insights are delivered. Within a couple of hours, all the information needed on decision-makers' dashboards and public-facing e-government portals is refreshed and ready to be acted upon.

Our study on leveraging edge computing for property encroachment detection vividly demonstrates this workflow to solve a real-world problem, of relevance to metropolitan governance.

It is thus possible to bypass the complexities associated with regular satellite image-centric monitoring solutions. By delivering timely information to city authorities, one can empower informed decision-making and drive effective city management initiatives.

In Part-2, we will dive deeper into a few specific application areas in order to support city management and smart city initiatives in developing nations.

Early Detection - VISIBLE TO THE SKY!

According to the 2022 Global Annual Tree Cover Loss by Dominant Driver - Global Forest Watch, 2,28,190 sq. km of deforestation occurred due to anthropogenic activities like urbanization and commodity-driven deforestation.

In Mongabay's investigative piece "Meeting an Illegal Logger" by Robert Eshelman from 2014 , the illegal Indonesian logger revealed that logging to load a complete tractor with wood can span approximately 6-8 hours. Logs are then transported via river using boats. (We recommend Robert Eshelman's article to immerse yourself in the real and tough conditions that push one into this profession.) The signs of logging activity, like sawmills and log rafts, are often clearly visible from the sky!

Early detection of such activities is crucial for authorities to take action and reduce deforestation.

Capacity Building to Combat Deforestation

Increasing the frequency of monitoring and reducing the time between early warning analysis (using satellite images with predictive models) and early warning alerts is crucial to building local capacity to combat deforestation and promoting transparency globally.

Unsurprisingly, forest patrolling has human resource shortfalls and logistical bottlenecks. On the data side, satellite imagery available to teams has temporal gaps, producing an alert has high computational costs and needs specialized experts.

In light of the data limitations, researchers have started modeling deforestation activity to provide a more strategic approach as depending on overhead imagery (drones or satellites) for tactical response becomes expensive very quickly. By analyzing spatio-temporal patterns of illegal logging, we can find proxies for logging activities with the available data (open-access USGS and Copernicus satellites). Correlating these proxies with single data source pixel pattern differences and time-series analysis of multi-source data could be used to trigger alerts, albeit with temporal blind-spots.

Annotating satellite imagery using patterns intrinsic to illegal logging provides a foundation for building effective early warning systems. These systems rapidly detect these signatures in every new image, providing an alarm for illegal and unsustainable activities that might otherwise go unseen.

Temporal gaps in satellite imagery, and prohibitive computational costs have an indirect effect on the models themselves. You can see only what you can afford to - and in that lies an artificial limit to the currently active early-warning systems.

Take the example of Forest Alert a real-world example of the work done in partnership between the University of Leicester and the Kenya Forest Service (KFS). Built upon the data collected by the Sentinel-2 constellation, Forest Alert detects incidents every five days and generates notifications within 24 hours. If we assume a clearing rate of four hectares per day, we can potentially lose 24 hectares of forest area by the time the alert hits the patrol team's device.

The effectiveness of early warning systems depends on the frequency and availability of satellite data, and any compromise will hinder its performance.

Our primary need is timely alerts!

As soon as the system detects deforestation and triggers an alert, the rate of deforestation should decrease.

To detect illegal logging activity more effectively, we require satellite data that is acquired more frequently.

By utilizing a combination of satellites such as Landsat and Sentinel, each with different revisit days, we can align our observation calendar to access more satellite data at no cost.

If our budget permits, we can procure commercial satellite images to guarantee that each day,a new satellite image will be appended to our database.

However, obtaining more satellite data results in a significant need for greater storage and computational capacity since the image capacity increases manifold when the spatial resolution of the satellite image rises.When the resolution of satellite images doubles, the pixel size increases fourfold.

Nonetheless, the requirements for data storage and computational infrastructure remain high.

Even if we receive an image every 24 hours, the analysis required to trigger the alert can take an additional 24 hours. So, we lose up to 2 days from the incident to even thinking about on-ground actions.

Leveraging Space Segment

The two major segments of the deforestation early warning system are ground operations and the imaging satellite. The former serves as the instrument to enforce laws and neutralize threats, while the latter enables rapid monitoring and detection over areas larger than the ability of a ground team. The space segment feeds data to the ground segment, enabling timely warnings and interventions.

On-board edge computing in focus

While the revisit can be improved through access to more satellites, the processing latency cannot be sustainably solved by just increasing raw computing power. In industries like manufacturing and transportation, this has been achieved by bringing compute and algorithms to the sensors, thereby speeding up decision-making. In our scenario, this would mean that our AI model needs to run on the imaging satellite literally! This is actually possible, and the experts call it satellite edge computing.

If trained models are deployed on satellites equipped with on-board edge computing, tailored monitoring of high-risk regions susceptible to deforestation can be achieved. This capability is essential as the net latency in monitoring of deforestation incidents is critical for timely intervention.

By processing data directly on the satellite, the system minimizes delays associated with transmitting raw data to ground stations.

Moreover, this technology allows for customization such as receiving only area of interest images, only the affected area boundary, or just an alert message, ensuring that transmissions from the satellite deliver precisely what is needed for the teams on ground.

In the given scenario, the model deployed onboard is purposefully engineered to generate deforestation early warning alert messages directly from the satellite. With the ability to deal with only boundary files or text alerts, actual procurement of satellite imagery is, for all practical purposes, eliminated. Need for resident experts in data analysis can be reduced and so is the cost of storage and compute while still achieving the critical goal of delivering timely early warning alerts.

Figure 4 (A sample scenario) illustrates how on-board edge processing can empower daily deforestation monitoring by increasing the number of alerts, leading to more field interventions. This approach can reduce temporal gaps to mere hours, while simultaneously addressing data storage, computation, and associated cost limitations.

Value additions from On-board edge computing to deforestation early warning systems

• On-time alerts: Faster analysis enables immediate notification of deforestation activity, allowing for quicker response by field teams and authorities.

• Efficient resource allocation: Field teams can be deployed quickly to areas with confirmed deforestation activity.

• Improved trend monitoring: Continuous analysis allows for early detection of emerging deforestation patterns.

• Empowering local authorities with transparency: Faster alerts enable timely enforcement and build trust with communities.

• Global collaboration: Timely data empowers stakeholders like scientists and NGOs to monitor deforestation trends and track the effectiveness of conservation efforts.

Time to trial Space-AI for combating deforestation?

Deforestation undeniably contributes to global warming, with a cumulative annual release of approximately 4.8 billion tons of carbon dioxide into the Earth's atmosphere.

Carbon offsets, intended to mitigate greenhouse gas emissions, particularly those stemming from deforestation, are facing scrutiny due to their limited effectiveness in combating global warming. only 5.4 million out of 89 million credits (only 6%), actually resulted in carbon reduction through forest preservation,scientists reported in Science (Aug, 2023).

Early warning systems should be viewed as a technology enabler to make programs such as REDD+ work.

As we explore the potential of On-board edge computing in deforestation monitoring, it is time to trial this innovative approach on a larger scale to achieve more sustainable forest conservation outcomes.

Introduction

Encroachments are value-depreciating events for any parcel of land. Sequences of encroachment can cause not just inconvenience to any property but also have financial impacts for residential as well as commercial land, utility installations like cross-country pipelines and power transmission, mining blocks as well as railway lines.

Arya, a product developer studying the above, saw it as a market opportunity and listed more thrilling details. he came across real-world scenarios that played out in the recent past and the size of this unmet need:

• "Forest land five times Delhi's geographical area under encroachment, govt data shows. The latest data showed that 750,633 hectares or 7,506.33 square kilometers of forest land has been encroached in the country." - Deccan Herald (05 January 2024)

• In the USA, the direct financial impact of vandalism and theft in vacant properties is significant, with average losses ranging from $2,000 to over $20,000 per incident. - The Urgent Need for Security in Vacant Commercial Properties: A Comprehensive Guide - 06 - Feb -2024

• The robust growth (8.27% CAGR 2024-2028) in the security services market highlights an increasing demand for private security. This demand is intricately connected to the issue of property encroachment, as property owners seek security services to defend against boundary violations.- Statista

Objectives for Effective Solutions

Enthused by the numbers he's seen, Arya looks into the services addressing property encroachment issues.

The existing solutions vary from assigning security personnel to installing CCTV cameras and using drones. These have been the top touch-points for most property owners in tackling encroachment.

At the back of his mind, Arya understands that these techniques have limitations staring from the actual encroachment detection to reporting. For example, malpractice is possible while engaging security personnel; monitoring vacant land on a large scale with cameras is not really practical and so goes for drones as a deterrent considering the lack of regulatory clarity and the risk of injury to humans on the ground.

From the survey, Arya derived a few objectives that a typical service should deliver:

• Objective - 1 : Ensure effective monitoring by detecting encroachment in a timely manner.

• Objective - 2 : Communicate the essence of the incident to the stakeholder with the least delay.

• Objective - 3 : Cost-effectiveness - Cover budget aspirations of property owners at different levels and different categories of properties.

Searching for scalable solutions, Arya found that "Remote monitoring" addresses the limitations of the on-site solutions he has seen so far. Remote sensing fused with geospatial technology seems to be already addressing these challenges, and the ability for better encroachment detection is surging with all the new satellites being launched to space!

When digging deeper into this, Arya finds that high-resolution satellite images are a critical input.

Arya encounters Aleks Buczkowski's blog, which discusses the market availability of satellite imagery, particularly emphasizing the "Minimum order area" and pricing based on dollars per km².

Though this solution is technically more effective, Arya found a gap in cost-effectiveness when trying to sketch out a prototype solution.

For monitoring a vacant parcel of land spanning 0.23 km², Arya tries to collect the combined solution cost for the top-rated offering today (CCTV with motion-detection and ability to have an armed security person on call) and compared that with a solution that includes an option to leverage the satellite image-based idea.

He realizes that a high resolution satellite image covering 100 km² has to be acquired from the market, to monitor his precious 0.23  km².

Also, Arya finds that the satellite images will always remain under-utilized and become a pain-point.While visualizing the business scenarios, Arya came up with two.

1. More properties to monitor in a smaller region.

Here, a satellite image was acquired for 100km² (smaller region) in conjunction with more properties to monitor, assuming even a measly three-fold of the security and CCTV customers, then using satellite image for this scenario looks competitive.

2. More properties to monitor at the State/Country level.

If Arya were to service property owners country-wide, then he would certainly get more customers, but he would need to procure a massive number of satellite images.  

For example, if ten properties each spanning one km² are spread across ten different US states, then imagery to be acquired would be 1,000 km², and the utilization ratio would stand at 1% even after the sizable spend.

The existing and traditional methods seem more viable cost-wise in this scenario, but the first two objectives will still need to be addressed.

Arya understands that scaling the encroachment-monitoring service to the state/country level turns the business potential from excellent to vulnerable due to the meager utilization rate of images. If one compares with high-profitability SaaS businesses, this current iteration of the monitoring solution seems to be going in the wrong direction - the cost to maintain a larger customer base just keeps going up!  His conclusion is that the utilization rate of satellite imagery procured has to be high in order to result in sustainable and profitable growth of his business.

Area-of-Interest Matters

Arya, after a week of thrashing out his ideas with friends and over caffeine shots in cafes, comes up with a seemingly obvious question:

"A satellite image is required to detect encroachment. But for 100 km² or the area of interest?"

He blurts out, loudly, "Image of the Area of Interest!".

This epiphany ignites another search, "Is it possible to get an image of just the area of interest?"

Arya calls up his old school friend, now a professor or Remote Sensing at a prestigious university, and tries his luck. Dr. Neil winces at the question with a knowing smile and explains, "I have had to defend satellite imaging so many times, especially when this specific question comes up. The field of remote-sensing has been a boon for us scientists, but it hasn't really changed to cater to commercial needs where costs need to be controlled in relation to the final offering's market potential and end-user preferences". After giving a long pause, Neil has a twinkle in his eye and says, "There is a way, and this is a new development where the satellite, when provided with the area of your interest, can figure out which parts of the image lie within it, and send only that to the user. This is an simplified explanation for a complex process that is being built and tested as we speak by researchers. The costs can come down by 30-40% and over time really become proportional to the area of a customer's interest!"

This means, the answer is YES! Okay, maybe as Neil says it will take a year or more for the engineers and scientsist to perfect this, but the answer remains very ecouraging. Neil points Arya to the keywords "edge computing" and "onboard processing" when looking for a way to bridge the gap in his product.

Onboard Edge computing enables delivery of something called "Smart-tasked AOI Image", which is an image of our area of interest, eliminating the minimum order area as well as unnecessary images that might not have clear visibility of the location due to cloud cover.

So, with edge computing combined with a high resolution imaging satellite, the running costs for data acquisition becomes controllable over time, the only job left to be done is automating Encroachment Detection through Machine Learning.

Once the technology and communication choices are made; the question of cost-effectiveness must be answered.

Arya is redefining the needs once again:

• A satellite image for the AoI has to be acquired at the least possible cost.

• The overall storage and processing costs have to be low.

Images will still have to be stored on the cloud, high-availability compute needs to require cloud compute - all have costs associated, and storage costs will only grow. Larger the data footprint per user per inspection, higher the slope of the recurring costs.

Arriving at the opt-solution

Armed with the understanding of 'edge computing', Arya determines that what his customers need is mostly the notification of encroachment, and image is needed at best for visual confirmation - not a heavy TIFF file, just a JPEG thumbnail. He could bring the encroachment detection model closer to its source: "The satellite itself"!

To harvest the benefits of satellite edge computing and provide a commercial service through the value chain, the model has to be placed on the satellite.

Imagery takes second place. When the encroachment is detected onboard by the satellite, a notification needs to be sent to the user - just some text detailing the nature of the encroachment needs to be conveyed. If the user opts for a visual confirmation, an compressed image can also be linked - maybe this can be a premium feature!

Text and a compressed images take very little storage space, ground-side processing time, thereby reducing the total cost of solution, delivering the optimal goods to meet the objectives: technology, communication, and cost-effectiveness. Now, no need to worry about customer density. It is viable to serve a much more significant customer-base irrespective of geographical spread.

Arya finds that, as a product developer, it's crucial to address the limitations of CCTV & physical monitoring services by bridging gaps rather than replacing it with a single one-size-fits-all solution. Building trust incrementally, and also factoring in the limitations of today's satellites (persistent monitoring is not yet there with high-resolution satellites, and inspecting the interiors of a building is still sci-fi).

Through ample study of customer preferences, we regard text notifications and visual imagery for inspecting encroachment as complementary enhancements to physical security personnel and mobile-connected CCTV offerings in the market. Furthermore, these features hold the potential to serve as standalone products over time.

Arya starts working on his product, with an understandably geeky and un-exciting name "EncroWatch". But hey, the product is exciting!

This is not an imaginary product. The App depicted has the potential to hit the market through the endeavors of many product developers like Arya, ably supported by innovative researchers and startups worldwide working to bring the benefits of edge-computed insights to one and all!

Financial Crisis to AI-Powered Solutions

In California, a renowned insurance company is facing financial instability due to wildfires.

Wildfires are becoming increasingly frequent and widespread, as evidenced by recent data. As summarized in this report by the World Economic Forum, "As climate change causes more natural disasters, the insurance industry is raising rates, reducing coverage and exiting some markets entirely".

The need of the hour is to equip these tough and very real scenarios with tech solutions. Recent advancements, such as AI, ML, and their fusion with satellite imagery and geospatial technology can empower service providers who offer analytical services on assessing wildfire risks, thus serving insurers and communities well. Used judiciously by the data scientists and analytics teams, this can really change the game for insurers!

And this is not all! More recent innovations can further power these tech solutions.

For example, imagine a deep-learning model trained to detect areas at risk from wildfires utilizing satellite imagery, running on every satellite looking at Earth, continually - with assessments delivered directly to the insurer's decision-making tools. Sounds futuristic? Not at all!

This article describes the transformative capabilities of edge-computing paired with satellite imaging and its potential.

On-board processing - Supercharging Wildfire Financial Risk Solutions

The model, trained to assess the property-centric wildfire risk, is on-boarded. The satellite on which this model is on-boarded will possess all relevant tools:

• Platform to accommodate the model

• Capacity for earth imaging

• Provisions for Geo-processing and generating input for the model

• Ability to run the model and extract the final output

• Transmitting the property-centric assessment.

The satellite will directly transmit the property-centric assessment to the insurance company's decision-making tools. Based on this assessment, the premium will be determined according to the current scenario. Similarly, for claims, damage can be assessed using the same method.

The up-to-date details and the holistic process make this a hassle-free, win-win situation for the company and customers.

What is actually happening? - Here, one does not order satellite imagery; rather the insight.

Is this just bells and whistles added to traditional remote sensing? - We have come a long way since the advent of satellite imaging as it began in the 1960s - from manual visual interpretation from printed 1:1 image, to convert insights to decision-making in an automated workflow. While the key technical processes remain largely the same, the speed at which we can arrive at meaning from pixels has increased exponentially. Executing this at the edge, on satellites eliminates the latency and with AI/ML, this process can only get better - better than the human eye!

Feasibility: Coming to the technicalities, it is possible today to connect a real-time feed of satellite images to your model and configure it to run in an automated way, with the key to governing its behavior held by you - the model's creator!

Better Perspectives: Financial resilience: The on-board edge computing is designed to smart-task processes like radiometric correction, image quality enhancement, and geometric correction on the raw satellite image and produce the cloud-free AoI image, then feed this into the model on-boarded to deliver targeted data insights.

Focus on the need - Delivering Focus of Interest: The smart tasking mentioned above helps focus on the area of interest, which could be a minimal portion of the larger image, for example, 10% of the pixel ratio over the larger tile of the high-resolution image.

Critical inputs for financial risk discussions: Delivering the Data of Interest: To make decisions on financial risk-related issues, the risk management team needs key inputs like the quantum of damage, areas to be focused on, and number of property damages. Edge computing platforms can deliver these details to the decision-making tools from the satellite.

Continuous improvements: Configurable outputs: Once the model is operational on the satellite, the platform ensures flexibility in updating the model, which may be necessary due to factors such as policy changes, climate variations and geographical reasons thereby influencing the outputs. This feature underscores the control of the model creator over the model's governance.

On-time details - Time-saving: Edge computing helps to extract pixel subsets of the area of interest, and numeric derivatives, result in fast data transmission to the decision-making tools. This faster transmission is possible by the memory footprint compression.

Value propositions from On -board model and On-satellite Analytics:

• Significant cost reduction in wildfire monitoring and Property/Area of Interest risk assessment, since the whole process concerns areas of interest.

• An insurance company's decision-making tools are updated with timely details due to a faster transmission of data, supporting quicker decision-making.

• Elements like targeted notifications and fire detection models enable proactive mitigation, reducing the financial burdens of an insurance company and reinforcing better administration.

• Remote updating the on-board model, making this process long-running and adaptable for better long-term assessment and continuous improvement.

Advancing financial stability

In response to the escalating threat of wildfires, insurance companies require comprehensive insights and timely data to ensure efficient financial management. On-board edge computing emerges as a pivotal solution, seamlessly integrating data into wildfire-related financial risk management processes. This innovative product fosters resilience within the insurance sector by providing timely insights for informed decision-making and proactive risk mitigation.

Join the innovation

SkyServe laid the foundation for this product, as detailed in this blog, "SkyServe Partners with SatRevolution for the 2023 STORK Launch."