By Nikolaas Steenbergen, CEO and Founder, 030Solutions

The evolution of sensor technologies such as lidar, cameras, and drones has been pivotal for the GIS industry. These technologies have become more sophisticated yet increasingly affordable and accessible in the recent years. Lidar sensors now offer unprecedented accuracy in 3D mapping, while enhanced camera technologies provide finer details and higher resolution imagery. Drones, on their part, have become more efficient and cost-effective, enabling extensive aerial data collection even in previously inaccessible areas outside, even hand held and backpack mounted lidar and image systems are available.

With the influx of data from these advanced sensors, the GIS industry faces a significant challenge: the need for efficient data processing. The amount of data needs to be reduced in such a way to be able to derive actionable information to be useful. Manual labeling and analysis of this data are not only time-consuming but also expensive. This bottleneck necessitates a shift towards automated solutions, where AI plays a crucial role to be fast and scalable.

Even though current AI based methods play an increasingly important role in the GIS industry, research and development from foundational methods are far from reaching a standstill. Cutting-edge algorithms in machine learning and deep learning continue to evolve, enhancing their ability to interpret complex geospatial data more accurately and swiftly, especially in the area of point cloud and image analysis and photogrammetry. This ongoing progress and its practical applications is further going to accelerate the usage of image and lidar technology in the GIS industry for more sophisticated data analysis.

An increasing number of companies in the GIS sector are embracing AI. This adoption is driven by the need to stay competitive and to handle the ever-growing datasets more efficiently.

The integration of AI in GIS is leading to enhanced automation and digitization across various domains. For example, AI algorithms swiftly analyze imagery to identify conditions of roads using mobile phones. Surface cover type analysis for civil engineering projects like broadband expansion is another area benefiting from this technology, enabling more precise planning, execution and monitoring. Asset inventory, infrastructure maintenance, and expansion are also seeing significant improvements, with AI providing more accurate, timely, and cost-effective solutions.

In addition, we are and will increasingly witness a broader application of these technologies in areas such as environmental monitoring, urban planning, and disaster management, where real-time data analysis is crucial for decision-making. 2024 will see further adoption and new application of these technologies for industry leaders as well as startups introducing radically new approaches.

The advancements in sensor technology, coupled with the relentless progression of AI, are setting the stage for a new era of smarter, more efficient, and resilient infrastructure systems. As companies continue to adopt these technologies, we can expect a future where our physical and digital worlds are more interconnected, leading to smarter cities and communities across the globe.