Environmental Oracle

[IMPACT] Please describe your proposed solution.

Overall challenge

The urgent need for sustainable solutions is driving a rapid adoption of smart environmental monitoring and diagnostics technologies that utilise spaceborne Earth Observation (EO) and climate data. Target applications encompass a wide range of commercial domains, such as natural resource management, disaster management, weather forecasting, precision agriculture, forestry, mineralogical prospection and global trade. Furthermore, the emerging market for carbon credits and other sustainability services, such as direct carbon capture, regenerative agriculture, reforestation and afforestation require careful monitoring and validation to achieve the desired level of credibility and trust.

Unfortunately however, multiple major technological bottlenecks continue to impede wide adoption and commercial utility of environmental sensing technologies beyond the limited scope of scientific research. We have conducted an extensive survey of multiple organisations that aim to promote and accelerate reforestation, afforestation and soil carbon restoration, including for example Open Forest Protocol (https://www.openforestprotocol.org/), Restore (https://restor.eco), The Pond Foundation (https://thepondfoundation.org/), Landano (https://www.landano.io/), Forest Conservation Fund (https://www.fundforests.org/) and Agora Carbon Alliance (https://agorocarbonalliance.com). 

Our findings show that at this time the vast majority of monitoring and validation for such projects relies on labour-intense, expensive, time-consuming and often unreliable ground samples and manual inspection. Significant efforts have been invested into automation of monitoring and validation using remote sensing technology, but the scalability and efficiency of such methodology is currently limited by the following factors:

• Commercial high-resolution EO imagery is far too expensive for most environmental monitoring projects and applications, while the spatial resolution of the low-cost EO imagery is too coarse for most such applications.
• Challenging atmospheric conditions, such as obstruction by clouds and cloud shadows, make the availability of high-quality data highly irregular and temporarily incoherent.
• Monitoring often requires high-quality historical imaging data that is often unavailable, or too expensive.
• Each EO system is designed to address a specific segment of use cases using a specific combination of technical characteristics, such as revisit time, number of spectral bands, spectral range and spatial resolution, thus making automated assimilation of data from multiple such systems difficult, or impossible.
• Radiometric calibration, and thus the stability and reliability of collected data varies drastically between different systems and continues to present a major and largely unsolved technological challenge.

Significant attention has been recently raised by the Dynamic World by Google (https://www.dynamicworld.app/) – “A near real-time land cover dataset for our constantly changing planet”. This system clearly demonstrates the immense potential of remote sensing for environmental monitoring applications. The system addresses the very important challenge of global coarse land-cover classification into nine major types, however it does not provide the means to understand the local environmental dynamics for a specific project or use case.

Our vision

The proposed project will develop and deploy an Environmental Oracle that will offer a consolidated source of regularised multi-modal multi-scale (spatial, spectral and temporal) environmental data required for implementation of low-cost, scalable monitoring and validation solutions for carbon capture and ecological restoration projects and DApps. The proposed data structure will include a fusion of spatial, spectral and temporal features specifically designed and optimised for training of analytical and predictive ML models.

The information aggregated by Environmental Oracle will act as a form Environmental Passport for any target plot of land that will both record all changes of status, as well as provide a source of objective data for the analysis of dynamics, performance and health indicators.

Figure 1. H3: Uber’s Hexagonal Hierarchical Spatial Index

The proposed Environmental Oracle will utilise Uber’s Hexagonal Hierarchical Index (H3) for multi-scale indexing and access to consolidated environmental data. The system will provide access to the following data types in the form of regularised and consolidated multi-layer multi-modal data cubes:

• Instant
• Radiometrically calibrated, cloud-free multi spectral satellite imaging data at up to 2m/px spatial resolution
• Short-term cycle – 12-month time series at 5 days revisit rate
• Reflectance spectrum
• Climate data
• Temperature (max/min/5-day average)
• Precipitation (5-day average)
• Cloud cover (5-day average)
• Long-term cycle – 12-year time series at 60 days revisit rate
• Vegetation index
• Climate data
• Temperature (max/min/60-day average)
• Precipitation (60-day average)
• Cloud cover (60-day average)

Figure 2. Ecomandala visualisation of the information features constituting the Environmental Oracle multi-modal spatial-spectral-temporal dataset.

The resultant consolidated source of environmental data will be deployed on Cardano blockchain and will be made available through a standardised API which will significantly streamline and simplify access to such data for developers of environmental monitoring and validation applications, as well as a wide variety of sustainability focused DApps. Some examples of such DApps could include DeFi support of regenerative agriculture, agriculture supply chains, forest conservations and tree planting projects.

As part of our future work, we are planning the development and release of an Ecomandala NFT that is a dynamic visualisation of the aggregated and regularised environmental data detailed in Figure 4. Ecomandala will represent the historical record and the current state of any given plot of land in an information-rich and visually compelling form allowing for interactive and engaging exploration of environmental data.

Figure 3. Original Sentinel-2 L2A scene (left) and processed scene with the clouds and cloud shadows removed using the proposed data fusion method (right).

The proposed project will invoke the latest developments in the area of Artificial Intelligence and Machine Learning to enable coherent fusion of multiple sources of environmental data, including European Space Agency (ESA) Copernicus Sentinel 1 and 2, as well as the corresponding climate data in order to obtain regularised, cloud and cloud shadow-free time series of satellite imaging data.

The satellite imaging data will be further super-resolved to the spatial resolution of 2 m/px, significantly expanding the range of target monitoring and verification applications.

Figure 4. x10 super-resolved images generated by Gamma Earth S2DR model. Sentinel-2 RGB 10 m/px (left), S2DR RGB 1 m/px (center) and Google Maps RGB 30 cm/px (right).

Project Work Packages

The proposed Environmental Oracle will incorporate the following major Work Packages

1. Ingestion and assimilation of multi-date Copernicus Sentinel 1 and 2 data
2. Detection, masking and removal of clouds
3. Super-resolution of EO imagery to 2 m/px spatial resolution
4. Fusion of EO imaging data and climate data into single multilayer spatial map
5. Implementation of Environmental Oracle
6. Design and development of Web portal for data visualisation and access
7. Development of Python API

Importantly, Work Packages 1-3 will constitute the direct extension of the previously attained results and only a minor refactoring and improvements is envisioned within the scope of the proposed project. The current proposal will be mainly focused on the implementation of Work Packages 4 - 7.

[IMPACT] Please describe how your proposed solution will address the Challenge that you have submitted it in.

The envisioned Environmental Oracle will help resolve a major technological bottleneck associated with the challenge of sourcing, accessing and processing relevant environmental data required for monitoring, auditing, and validation of carbon capture and ecological restoration projects. It will enable the development and deployment of a new class of high impact DApps on Cardano blockchain positioning Cardano as a leader in environmental monitoring applications.

Environmental Oracle will constitute a platform and an integration layer enabling further development and deployment of monitoring and validation solutions for specific DApps on Cardano ecosystem. We therefore inspire to

• Showcase the methodology of low-cost scalable monitoring and validation of ecological restoration projects
• Standardise, streamline and simplify access to consolidated environmental data
• Attract the developers of monitoring and validation solutions for carbon capture and ecological restoration projects to Cardano ecosystem

Our solution will further promote collaboration, synergy and interoperability between Cardano projects by leveraging the achievements of the recent Fund 8 “Oracle Development Portal” project: https://cardano.ideascale.com/c/idea/400771 (which is not a related project but we will be happy to collaborate in order to maximise the impact of both projects).

Examples of funded and submitted Cardano Ideascale projects with direct synergy potential to the proposed Environmental Oracle

• 21st century Agri supply chain https://cardano.ideascale.com/c/idea/403695
• Oracle Developer Portal https://cardano.ideascale.com/c/idea/400771
• Indigenous Land Rematriation https://cardano.ideascale.com/c/idea/398750
• Open ledger for agricultural land https://cardano.ideascale.com/c/idea/400812
• Protecting wildlife & Maasai, Kenya https://cardano.ideascale.com/c/idea/399158
• Landano: Cardano land registry Dapp https://cardano.ideascale.com/c/idea/381957
• Community validation Dapp https://cardano.ideascale.com/c/idea/421218

[IMPACT] What are the main risks that could prevent you from delivering the project successfully and please explain how you will mitigate each risk?

Type of risk: Technological

Description of risk: The envisioned system implies ingestion and assimilation of heterogeneous multi-modal environmental data from multiple sources and data providers. The respective end points may be subject to change over time, thus making the system prone to outages.

Effect of risk: Reliability constitutes one of the major advantages and selling points of the proposed system. Consequently, any reliability issues can have significant negative impact on long term adoption.

Mitigation methods: The system will require careful planning of performance monitoring and maintenance mechanisms. Furthermore, system robustness mechanisms will be implemented that will allow the system to remain online even when some of the input signals may be temporarily unavailable.

Type of risk: Economic viability

Description of risk: The sourcing, processing of data will have significant cloud infrastructure and computing costs

Effect of risk: As is the case with any technological system, the long term success will be put at risk if the economic viability is not realised from an early stage.

Mitigation methods: Careful attention will be given to the cost management and computational efficiency of the system. Furthermore, we will continue to continuously monitor the cost-benefit balance of the proposed solution and will seek to reach a break even point at the earliest possible stage.

[FEASIBILITY] Please provide a detailed plan, including timeline and key milestones for delivering your proposal.

WP1: Ingestion and assimilation of multi-date Copernicus Sentinel 1 and 2 data - already DONE

• Design of system architecture
• Design and deployment of data storage, management and access infrastructure
• Implementation of ingestion API
• Implementation of assimilation pipeline

WP2 Month 1 – Month 3: Detection, masking and removal of clouds - 80% DONE

• Development of algorithmic workflow for the fine-structure alignment of consecutive Sentinel-2 scenes
• Development and integration of high-precision cloud masking on Sentinel-2 scenes
• Training, validation and optimisation of predictive cloud-removal model

WP3 Month 2 - Month 4: Super-resolution of EO imagery to 2 m/px spatial resolution: 75% DONE

• Modelling of Sentinel-2 imaging datasource
• Generation and augmentation of synthetic training data
• Model training and validation
• Design and deployment of scalable processing infrastructure

Focus of this proposal:

WP4 Month 1 - Month 4: Fusion of EO imaging data and climate data into single multilayer spatial map

• Design and development of data fusion pipeline
• Data management infrastructure and integration of H3 API

WP5 Month 2 – Month 5: Implementation of Environmental Oracle

• Cardano blockchain development
• Design and deployment of access API
• Testing and Quality Assurance

WP6 Month 3 – Month 6: Design and development of Web portal for data visualisation and access

• UI/UX design
• Web development
• Backend development
• API development

WP7 Month 4 – Month 8: Development of Python API

• Python API development
• Technical documentation
• Testing and Quality Assurance

[FEASIBILITY] Please provide a detailed budget breakdown.

A significant portion of technical and scientific development for the proposed project has been already carried out and self-funded. We are seeking complementary funding to enable the deployment of the proposed Environmental Oracle on Cardano blockchain, as outlined in Work Packages 4 – 7.

Proposal funds requested: $138,000

The budget is divided over 6 months work and therefore a monthly budget of $23,000. We decided to apply for the amount that will enable us to deliver a full proof of concept and not partial elements for a shorter time frame. We believe there is also a potential to start generating revenue within this time frame and we will evaluate other funding opportunities for the future. Overall, the success of this proposal could have a significant effect, potentially positioning Cardano as a leader for environmental and sustainability Dapps.

WP2 Detection, masking and removal of clouds: $10,000

We estimate 200 hours of data science and software development at an hourly rate of $50 to advance an existing prototype system to attain the following deliverables

• Cloud based system for sourcing, assimilation, regularisation, cloud masking, cloud removal and generation of cloud and cloud-shadow free time series of S2 images
• Web interface to demonstrate the cloud removal system

WP3 Super-resolution of EO imagery to 2 m/px spatial resolution: $20,000

We estimate 400 hours of data science and software development at an hourly rate of $50 to advance an existing prototype system to attain the following deliverables

• Cloud based microservice to super-resolve S2 data to 2 m/px spa
• Web interface to demonstrate the super-resolution system

WP4 Fusion of EO imaging data and climate data into single multilayer spatial map: $20,000

We estimate 400 hours of data science and software development at an hourly rate of $50 to attain the following deliverables

• Cloud based microservice to ingest, assimilate, fuse and organise multi-modal, multiscale data on an H3 hierarchical grid
• Web interface to demonstrate the data assimilation and visualisation system

WP5 Implementation of Environmental Oracle: $20,000

We estimate 400 hours of blockchain and software development at an hourly rate of $50 to attain the following deliverables

• Blockchain infrastructure
• On-chain data storage
• Cloud based microservice for on-chain assimilation of environmental data

WP6 Design and development of Web portal for data visualisation and access: $20,000

We estimate 400 hours of UI/UX design and software development at an hourly rate of $50 to attain the following deliverables

• Web-portal for on-demand generation, access and visualisation of fused environmental data

WP7 Development of Python API: $20,000

We estimate 400 hours of software development and technical writing at an hourly rate of $50 to attain the following deliverables

• Python packages for data exploration and access API
• Technical documentation of the Python API

Project management, legal, accounting and communications: $16,000

We estimate 320 hours in project management and related activities at an hourly rate of $50, as well as the following additional costs:

Cloud computing and hosting costs - $12,000

[FEASIBILITY] Please provide details of the people who will work on the project.

The Environmental Oracle team comprises experts with extensive experience in 

• Remote sensing, ML and AI
• Environmental monitoring
• Sustainable development and ecological restoration projects
• Blockchain development
• Geographic Information Systems

Yosef Akhtman, Technological lead

Experience:

Yosef is a technologist and founder with in-depth expertise in Remote Sensing, Environmental Monitoring, Geo-Information Systems, and Machine Learning. He is the founder of Gamma Earth, a research company founded in 2020 and focused on development of advanced Earth Observation and remote sensing technologies for applications in environmental monitoring and precision farming. Previously, he founded and led Gamaya – a Swiss startup in the field of precision agriculture, where he led the development and commercialisation of an integrated digital agronomy solution based on a unique combination of hyperspectral imaging, data fusion and artificial intelligence. 

Involvement: Leading the technological feasibility and system architecture

Links: https://www.linkedin.com/in/akhtman/

Magnus Edvard Nielsen, Product lead

Experience: 

Magnus is a product lead with 10+ years experience in building technology products, teams and companies in both B2B and B2C. He has a strong focus on lean methodologies; build, test and learn to stay close to customers, new market trends, increase product quality and speed of development. A Strong passion for web3, complex digital products, getting from “zero to one” and building a sustainable future for our planet.

In June 2019 travelled to Indonesia for research on Indonesian farmers within rice, coffee, coconut sugar.

From 2017-2021 Leading the innovation team, at one of the biggest PaaS companies within digital supply chains named Tradeshift. The innovation teams two focus areas was decentralised technology and environmental sustainability.

Involvement: Leading the product research and product development.

Links: www.linkedin.com/in/magnusedvard/

Scott Poynton, Sustainability Lead

Experience: 

Scott has been a real social and environmental leader for the last 30 years. He founded The Forest Trust (currently www.earthworm.org) NGO and his positive impact is estimated at $2 trillion of supply chains, as well as millions of hectares of forest. He is now on to his biggest mission to mitigate climate change.

Scott is already involved with the Cardano community. He presented at the Catalyst sustainable goals event: Cardano & Climate Change:

(00:30:18 Corporation, Trust and Blockchain, Scott Poynton). He is also part of an important related fund 7 funded proposal, the Cardano Carbon Footprint: cardano.ideascale.com/c/idea/385045 

Involvement: Leading the project within Ghana and enabling access to stakeholders of the supply chain.

Links: https://www.scottpoynton.com/about-1 

For more information please visit:

www.thepondfoundation.org

www.earthworm.org

https://adifferentway.life/

Yoram Ben Zvi, business models lead and connect to the Cardano ecosystem

20+ years of business experience working with technology companies (strategy, partnerships, investors). In recent years, Yoram is focused on combining impact and business. 4 years ago he left his comfort zone and worked for 2 years for an NGO Earthworm.org focusing on sustainable business models across agriculture supply chains.Yoram is very active in Catalyst as a CA, successful proposer, and at Cardano4Climate. Yoram is part of the AIM team and is involved with the Catalyst SDG tool cardanocataly.st/proposer-tool-sdg/#/ (which is included in the proposal process) and the catalyst alignment to SDG research. 

Yoram was working on a related project in Indonesia for one year with Earthworm.org together with Magnus and understand the opportunity and the importance of improving monitoring and verification of sustainability projects 

Involvement: Leading ecosystem and business model development.

Links: https://www.linkedin.com/in/yoram-ben-zvi-446836/

The technical implementation of the project will be carried out by multiple design and development teams:

• Web design UX/UI: Axicube: https://axicube.io/
• Software Web/GIS: Onix Systems: https://onix-systems.com/

Cordano blockchain: Zpoken https://zpoken.io/

[FEASIBILITY] If you are funded, will you return to Catalyst in a later round for further funding? Please explain why / why not.

If the project is funded we will seek to develop collaboration and synergy with other projects carried out by the Cardano community. We will therefor continue to leverage the network and expertise of the community and will consider to seek additional opportunities, including the future Catalyst funds, to further expand our work.

[FEASIBILITY] Are you or any member of your team working on any other proposals in this Fund9?

[FEASIBILITY] Are you or your team working on any other proposals from previous Funds?

[AUDITABILITY] Please describe what you will measure to track your project's progress, and how will you measure these?

A dedicated web platform will be created for the purpose of demonstration of the achieved results. The platform will showcase the performance of the regularisation and fusion methodology over multiple areas of interest. The platform will further enable the comparison between the currently available and synthesised EO data for the sake of validation, manual inspection and demonstration.

Furthermore, multiple case studies will be conducted to demonstrate the performance of the proposed method for the synthesis of EO data over urban, agricultural and natural environments. Additionally, we will seek to prepare scientific papers and/or white papers in order to document and disseminate the obtained results.

Progress of the development of the project will be measured by:

• Github commits for each sprint (2 weeks)
• Deliverables at each milestone (WP2-WP7)
• Google Analytics of the Web portal (ones on-line)

We will further seek to collect and document interest from carbon capture and ecological restoration projects interested in the use of the developed system for monitoring and validation. The collected inputs will be published in the project’s public blog.

[AUDITABILITY] What does success for this project look like?

The successful realisation of the project will constitute a functional Prof of Concept Environmental Oracle that will provide environmental data to one or more pilot user projects. The product environmental data will be generated and made available on-chain for a few selected areas of interests in support of the pilot users. The current target pilot will be carried out with the Agricultural supply chain in Ghana project (https://cardano.ideascale.com/c/idea/403695).

This is a game changing application for Cardano. The aim is to develop a world class sustainability Oracle that will lead to transparency and traceability of land use worldwide. This proposal aims to demonstrate the efficiency of blockchain technology and Cardano in particular for DApps focused on sustainable development. 

On top of this data we will design a range of potential use cases that could be essentials for key industries. For example tracking forest conservation projects or tree planting initiatives. Verifying land use impact on soil quality in the last 10 years and going forward - an essential information for agri supply chain practices and micro finance/ micro insurance for farmers. Support agriculture practices and many more applications that will be created using the environmental Oracle API.

[AUDITABILITY] Please provide information on whether this proposal is a continuation of a previously funded project in Catalyst or an entirely new one.

Entirely new proposal

Sustainable Development Goals (SDG) Rating

The nature of the envisioned projects lays itself to a particularly strong case for major contribution to the Green Deal across a list of sustainability objectives. In this section we focus on a number of key categories that allow for quantitative analysis, while broader impact is described in Section 4.10. Specifically, Open Platform for Global Environmental Intelligence has the potential to bring about a significant positive impact across the following Global Goals for Sustainable Development.

SDG 1 No Poverty: Global value chains are largely controlled by large international commercial organisations leaving local producers in the developing world on the brink of poverty. Access to timely environmental intelligence and market information will empower governments, corporations, as well as local communities and businesses to manage risks, increase profit margins, economic independence and their respective share of the generated wealth.

SDG 2 No Hunger: Both hunger and obesity can be regarded as undernourished conditions and represent major impediments for the betterment of global human wellbeing. Environmental intelligence will help predict and prevent major fluctuations in food supplies associated with drought, extreme weather patterns and climate change, significantly improving the efficiency and sustainability of food production systems and ensure stable access to high quality food.

SDG 6: Clean Water and Sanitation: Industrial production and agriculture in particular is currently responsible for 70% of all freshwater consumption on the planet. can facilitate significant optimisation and reduction in the use of this precious resource. A key factor for reduced availability of clean water is the massive deforestation caused by expanding agricultural production. GE can positively impact the situation by improving soil management and thus reduce the need for expansion.

SDG 13 Protect the Planet: Environmental Oracle aspires to make environmental data readily available for a wide range sustainability applications that require monitoring and validation. We are utilising sophisticated sensing and computational methods to enable feedback between our actions and the systemic traits of natural and agricultural ecosystems leading to a smarter and more sustainable use of natural resources.