AI News, Artificial Intelligence in Geoinformatics DWG

5.    Organizational Approach and Scope of Work

For example, k-nearest neighbor queries (a kind of Machine Learning algorithm) or Egenhofer’s nine-intersection model (a spatial reasoning approach) originate in AI and have been integrated with most geographic information systems (GIS) software.

AI has been considered in Geoinformatics as an active topic of geo-spatial knowledge discovery [2, 3, 4, 5, 6], such as vehicle trajectory prediction, indoor navigation, historical map digitizing, gazetteer conflation, geographic feature extraction, geo-ontologies, and place understanding.

GIS software and platforms are also applying DL algorithms (e.g., CNN [7], RNN [8], LSTM [9]) and frameworks (e.g., TensorFlow [10], Chainer [11], Caffe [12], Torch [13], MXNet [14]) to their data processing pipelines for better understanding the world around us.

Autonomous vehicles gather, manage, and analyze massive geospatial data from a combination of Global Positioning System (GPS), cameras, and other traffic sensors and require little or no human interaction based on rapid success of DL techniques.

Although recent AI for the geospatial data focuses on object extraction (such as roads and buildings) from remote sensing images, it is obvious that AI has tremendous potential to benefit a wide range of geospatial applications, including ‘digital twins’ (i.e., a digital replica of physical assets, processes, people, places, systems and devices).

The DARPA Explainable AI (XAI) program aims to create a suite of machine learning techniques that produce “glass box” models that are accountable to human reasoning with maintaining high performance levels [17].

For the safety, explainability, transparency, and validity of AI applications in dealing with spatial information, a collaborative effort among knowledge processing and representation, reasoning, planning and optimization, traditional machine learning, and deep learning is highly required.

This Domain Working Group (DWG) charter defines a role for OGC activities within Geospatial Artificial Intelligence (GeoAI) communities to provide an open forum for the discussion and presentation of interoperability requirements, use cases, pilots, and implementations of OGC standards in this domain.

The GeoAI DWG is chartered to identify use cases and applications related to AI in geospatial domains with its reliance on IoT (e.g., healthcare, smart energy), robots (e.g., manufacturing, self-driving vehicles), or ‘digital twins’ (e.g., smart building and cities).

This DWG will provide an open forum for broad discussion and presentation of use cases with the purpose of bringing geoscientists, computer scientists, engineers, entrepreneurs, and decision makers from academia, industry, and government to develop, share, and research the latest trends, successes, challenges, and opportunities in the field of AI with geospatial data.

The working group will aim to investigate feasibility and interoperability of OGC standards in incorporating geospatial information with AI and describe gaps and issues which can lead to new geospatial standardization to advance trustworthy and accountability for this domain community.

Recent significant impact of AI is leveraging not the improvement of only machine learning algorithms such as deep learning and reinforcement learning, but also the combination of the Internet of Things (IoT), big data, cloud computing, open-source software, GPU acceleration, and robotics, as well as algorithms.

For example, DL techniques with high-resolution satellite imagery are applied to classify the type of physical infrastructures like buildings, roads, and rails in a humanitarian mapping workflows [19].

For example, autonomous vehicles use geographic location information in integrating data from multiple sensors like cameras, radar sensors, lidar sensors, and ultrasonic sensors for their driving decisions.

If a training set is prepared for only a specific case and the user cannot understand factors driving model predictions, a decision-maker may face a critical risk of false positive or negative errors.

In deep end-to-end learning, the creation of training sets by distilling big data sets is more important than the traditional machine learning approach, which is based on feature engineering by human analysts.

Therefore, the role of skilled data professionals such as data scientists and data engineers “in the loop” for ML are essential to mitigate unintended biases and wider performance risks.

To this end, there is a need for developing sound policies, laws, and standards and for cooperation within the international community [21].” In a nutshell, the following benefits should be followed in the development of AI technology.

The GeoAI DWG is being established to address the applicability and gaps in the OGC standards baseline with regards to geospatial data in AI applications.  Although this group will not be the platform for creating new standards, it will be the platform to discuss and understand any issues, concerns, or barriers to interoperability for the geospatial AI community.

GeoAI is important for various participants such as geospatial professionals, local/national authorities, and industries to quickly and easily provide better geospatial insights, assess the impact of potential risks to an event, and improve quality of life in various domains: smart city, environmental management, smart transportation, disaster management, secure environment, etc.

·       Minimize incompatible technical distinctions between different AI application domains that utilize geospatial data, as this can lead to artificial barriers that limit the potential of all segments of the information community to come together and fully prosper;

The GeoAI DWG will concern itself with technology and technology policy issues, focusing on geospatial information and technology interests as related to the AI application domain and the means by which those issues are appropriately factored into the OGC standards development process.

1.     The mission of the GeoAI DWG is to document the use cases of AI applications that use geospatial data as well as identify opportunities of AI technology in geoinformatics in order to discuss the adoption of existing best practices and standards and/or recommend the creation of new standards to support the successful use of geospatial AI technology.

2.     The role of the GeoAI DWG is to serve as a forum for the discussion of AI-related topics brought to the DWG from OGC members as well as the larger AI community in order to determine the need for a standards working group (SWG) to further the creation of new standards required to facilitate the growth of geospatial AI technology and to present, refine, and focus interoperability-related issues to the Technical Committee.

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