Code and data for the paper "Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation"

A Look into GeoAI & GenAI as it applies to the Energy Industry.

The South Korea Geospatial Analytics market, valued at $820 million in 2025, is projected to experience robust growth, driven by increasing government initiatives promoting smart cities and infrastructure development, coupled with rising adoption of precision agriculture techniques. The market's Compound Annual Growth Rate (CAGR) of 8.44% from 2019 to 2024 suggests continued expansion through 2033. Key drivers include the need for efficient resource management across sectors like utilities, defense, and transportation, necessitating sophisticated geospatial analysis tools. Advancements in technologies like AI and machine learning are further enhancing the capabilities of these tools, leading to improved decision-making and operational efficiency. The market is segmented by type (Surface Analysis, Network Analysis, Geo-Visualization) and end-user vertical (Agriculture, Utility & Communication, Defense & Intelligence, Government, Mining & Natural Resources, Automotive & Transportation, Healthcare, Real Estate & Construction). While the precise market share of each segment isn't specified, the robust overall growth indicates strong performance across all segments. The presence of established players like ESRI and Bentley Systems, alongside innovative local companies such as Dabeeo Inc and GBS Korea, fosters competition and innovation within the market. The forecast period (2025-2033) anticipates continued growth, fueled by increasing data availability from sources like satellite imagery and IoT devices. However, factors like high initial investment costs for implementing geospatial analytics solutions and a need for skilled professionals to operate and interpret the data could pose potential restraints. Nevertheless, the overall market outlook remains positive, with the potential for significant expansion driven by the ongoing digital transformation across various sectors in South Korea and the increasing recognition of the value of data-driven decision-making. The market's growth trajectory is further supported by government investments in infrastructure modernization and the increasing emphasis on sustainable development practices. Recent developments include: May 2023: Planet Lab announced a new partnership with a South Korean startup Deajeon. This partnership will help the company to implement AI-powered Super Resolution and GeoAI Analytics, Object Detection, to satellite imagery. This will enhance the resolution to analyze changes and abnormalities with its GeoAI Pack., February 2023: Bitsensing, a South Korean imaging radar company, announced that the company is launching Traffic Insight Monitoring Sensor (TIMOS), which is a premiere monitoring sensor solution created to revolutionize smart traffic infrastructure.. Key drivers for this market are: Introduction of 5G to Boost Market Growth, Increasing Demand for Geospatial Analytics for Smart Cities Development and Urban Planning. Potential restraints include: Introduction of 5G to Boost Market Growth, Increasing Demand for Geospatial Analytics for Smart Cities Development and Urban Planning. Notable trends are: Increasing Demand for Geospatial Analytics for Smart Cities Development and Urban Planning.

Reality mapping experience combining imagery and GeoAI.

Parking lots in the USA occupy significant land area, particularly in urban and suburban areas. Using parking spaces for solar panel installation in the USA is a growing trend, known as "solar parking lots" or "solar carports". While solar energy has made substantial progress in the United States, there is still untapped potential. By installing solar panels on parking structures, it is possible to utilize this space for solar energy generation without requiring additional land. By doing so, it not only provides shade for parked vehicles but also generates clean energy and reduce the carbon footprint of buildings and facilities. They can also be combined with electric vehicle (EV) charging infrastructure, to estimate the potential demand for electric vehicles, which can be powered by the solar panels installed in the parking lot, promoting the adoption of clean transportation and reducing reliance on fossil fuels and further enhancing sustainability. But traditionally, parking areas are manually digitized and classified, which is a very labour and time-intensive task. Automating the task using deep learning models for parking space detection and solar panel capacity calculation outperforms traditional methods in terms of efficiency, accuracy, scalability, adaptability, real-time monitoring, and integration with renewable energy goals.The use of GeoAI for parking space detection and solar panel installation capacity calculation can have potential applications in urban planning, land use optimization, renewable energy deployment, sustainable transportation and contribute to the country's renewable energy goals. Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high resolution (30 centimeters -1.2 meters) imagery. For detecting small sized parking lots, higher resolution imagery is highly recommended.OutputFeature layer representing classified parking lots. Applicable geographiesThe model is expected to work well in the United States.Model architectureThis model uses the MMSegmentation based DeepLabV3Plus model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.75 and recall of 0.68.Training dataThis model has been trained on an Esri proprietary parking lot classification dataset.Limitations 1. The model is expected to work well on commercial paved parking lots. 2. The model might get confused with paved surface having similar reflectance.Sample resultsHere are a few results from the model. To view more, see this story.

De aarde warmt op en het klimaat verandert. Nederland moet zich voorbereiden op de risico's van het veranderende klimaat en hierop de omgeving aanpassen. Dit heet klimaatadaptatie. Voorbeelden van klimaatadaptatie zijn dijken verstevigen, rivieren verbreden en meer groen in steden en dorpen. Samen met de Provincie Overijssel en de Gemeente Almelo hebben wij 3 klimaatadaptatie cases gericht op Artificial intelligence opgesteld, namelijk: de detectie van zonnepanelen, de classificatie van groen/grijs en een bomen-analyse. De uitkomst van deze projecten kunnen helpen in beleidsvorming voor klimaatadaptatie op verschillende bestuursniveaus. In deze StoryMap gaan we in op de classificatie van groen.