At ARGO-E GROUP, we are always pushing the boundaries of technology to provide innovative and highly efficient solutions for our clients in geospatial analysis and urban planning. Today, we are excited to announce a groundbreaking addition to our suite of geospatial tools – a custom Python application designed to enhance the precision and efficiency of building footprint detection from orthophotos and 3D models.

As the demand for accurate building footprints continues to grow, whether for urban planning, land development, or disaster response, the need for highly effective and user-friendly tools has never been greater. Our solution not only meets this demand but sets a new standard in how professionals can interact with geospatial data efficiently.

The Power of 3D Models and Orthophotos

Drones mapping area of interest and then running photogrammetric algorithms to generate the orthophoto and 3D-mesh.

The core of our new application revolves around its ability to process a directory containing a true orthophoto and its associated 3D reality model (3D point cloud or mesh). True orthophotos, which are highly accurate and geometrically corrected aerial images, are commonly used in various industries for mapping and spatial analysis. By combining these orthophotos with 3D models, we can provide a much more detailed and accurate view of the built environment, allowing users to interact with buildings and terrain in 2D/3D environment and making it ideal for accurate mapping in urban areas.

The application's interface enables users to load both the orthophoto and the corresponding 3D model seamlessly. Once the files are loaded and geo-translated, the user can zoom in and out, rotate the model, and engage with the 3D objects in real time. This level of interaction is a game-changer for users seeking the highest accuracy when detecting and delineating building footprints.

The Python application once launched with the input files – the green footprints shows the user which buildings have already been detected.

Interactive Object Selection: Precision at Your Fingertips

Unlike other automated solutions that rely on machine learning or advanced algorithms to detect building footprints, our Python app takes a more hands-on approach. Machine learning models often assume the detection area is free from obstacles and that buildings are spaced apart according to standard construction practices, such as those in rural areas. While these models may struggle to distinguish between buildings and high vegetation or other large manmade objects, our app empowers users to guide the detection process by manually selecting buildings, reducing errors and improving accuracy.

In urban environments, where cars, roof shades, and other obstacles can obscure or distort building footprints, these models face even greater challenges. Our approach allows users to directly interact with the 3D model, selecting specific buildings with precision. By manually choosing the building of interest within the 3D space, users maintain full control over the detection process, ensuring that only the most accurate building footprints are extracted, even in complex environments. This unique method overcomes the common pitfalls of automated systems, such as misidentification caused by environmental factors like vehicles or roof structures. In addition, the user can easily add building information via a popup window with customizable fields, meeting specific user needs.

Exporting Building Footprints in Multiple Formats

Once all desired buildings have been selected and their footprint identified, our application makes it easy to export the results. The app generates both a JSON (.json) and a Shapefile (.shp) containing the building footprints and information data fields. These widely used formats ensure that the building footprints can be easily integrated into other geospatial software and workflows.

The JSON file, which stores geographic features in a lightweight format, is perfect for applications that require fast processing and flexibility. Meanwhile, the Shapefile, a staple in geospatial analysis, ensures compatibility with industry-standard GIS tools such as ArcGIS, QGIS, and other mapping software. Whether you're working on land-use planning, environmental assessments, or infrastructure projects, the ability to export footprints in these formats guarantees that the data can be immediately used in various professional applications.

Loading the .shp file in QGIS – showing the georeferenced objects for additional editing and correction (if needed).

Why Choose Our Solution?

Our application has numerous applications across industries that rely on accurate building footprint data. Urban planners, architects, and geospatial analysts can now efficiently detect building footprints for large-scale projects, improving decision-making and project planning. Disaster response teams can also leverage this tool to assess damage and plan recovery efforts by detecting building outlines and identifying structures at risk. Additionally, the ability to combine orthophotos with 3D models enhances the accuracy of flood modelling, fire risk analysis, and other critical assessments where precise footprint data is essential.

What sets our solution apart is its cutting-edge functionality combined with a user-centric approach. Unlike fully automated systems that rely on complex algorithms, our app provides professionals with the precision of manual selection, ensuring accurate results free from common AI-related errors. Its intuitive interface and real-time interaction empower users to extract high-quality building footprints tailored to their specific needs.

Beyond its accuracy and ease of use, our Python application is lightweight and highly customizable. We recognize that every project has unique requirements, so our app is designed to integrate seamlessly with existing geospatial workflows and support a variety of data formats. Whether for urban planning, disaster response, or environmental analysis, this tool delivers unmatched precision and adaptability in building footprint detection.

Future Directions and Expansion

At ARGO-E GROUP, we are constantly looking for ways to improve and expand our capabilities, and our new Python application is just the beginning. This tool is part of a broader vision to build a suite of geospatial solutions that empower professionals to unlock new insights and make more informed decisions. We are committed to innovation, and the success of this application reflects our dedication to providing advanced solutions that meet the evolving needs of the geospatial community.

Looking ahead, we plan to incorporate additional features to further streamline building footprint detection, including advanced editing capabilities, batch processing for large datasets, and support for additional file formats. By combining our deep expertise in Python development with a strong understanding of geospatial data, we’ve created a tool that enhances accuracy, efficiency, and usability—all while maintaining user control.

Get in Touch

If you are interested in leveraging our expertise for your geospatial projects or would like to learn more about our custom Python application, contact us today! We are always eager to collaborate on innovative solutions that drive progress in urban development, infrastructure monitoring, and beyond.

Stay tuned for more updates as we continue to refine and expand the capabilities of this powerful tool!