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GeoAI combines artificial intelligence (AI) with geospatial data, science, and technologies. In this paper, a successful case in utilizing GeoAI to improve the data quality in the national topographic database (TDB) of Finland was introduced. The project employed a GeoAI model to identify buildings from the input data of true orthophotos, digital elevation model (DTM), and digital surface model (DSM). The GeoAI-derived buildings served as reference data, enabling a comparison with building polygons from the topographic database (TDB) to reveal TDB building location deviations, missing structures, and demolished buildings. Throughout the project, algorithms were developed to match the TDB building vectors to the GeoAI-derived building polygons. The challenges include i) the differences between the GeoAI-derived building outlines and the TDB build footprints; ii) the reliability of the GeoAI model across data over different environments: urban, suburban, rural, and forest areas. Throughout the project, the GeoAI model was continuously improved by training massive new datasets: corrected vectors from the model prediction. Testing was conducted on datasets from twelve areas of Finland, covering 2204 km2 and including hundreds of thousands of buildings. The test areas covered urban, suburban, rural, and forest areas. The evaluation was conducted by the mapping team in the organization. The results showed that our methods greatly enhanced the quality of the TDB building footprints. The challenges and lessons of the project were addressed in the paper. [ABSTRACT FROM AUTHOR]

Automatizing the extraction of different objects from remote sensing data with deep learning methods has been a popular research topic. Buildings have been one of those popular objects to be extracted. Not only does the selection of neural network affect the results and accuracy of extracted buildings, but also the selection of different types of data for the task. Digital surface models (DSMs) are increasingly used in remote sensing and their demand has increased. Retrieving height information from surface models has proved helpful for accurate extraction of buildings. In this study was investigated, if the use of light detection and ranging (LiDAR) DSMs and DEMs with 25 cm pixel resolution will lead to more accurate building extraction results in comparison to the use of aerial DSMs. Results with UNet models trained with building vector labels, DSMs, DEMs and true orthophotos from multiple areas of Finland, were produced with different data combinations from two Finnish cities, Savonlinna and Pudasj¨arvi, to see, which combination would lead to the most accurate building detection results. Results were evaluated partly by visual inspection, and partly by quantitative assessment. Based on the tests carried out, combining the information from true orthophotos with LiDAR DSMs and 25 cm DEMs provided the most accurate results. In forest area, using LiDAR data increased the accuracy of building detection. However, in urban area, due to missing buildings from LiDAR data, its advantages were compromised. We suggest that the use of both imagery and LiDAR data should be the optimal solution. [ABSTRACT FROM AUTHOR]

Precise road segmentation is an essential part of many applications related to road information extraction from remote sensing data. The effect of color space on road detection has rarely been studied. In this paper, the effects of different color spaces of aerial images and multitask learning methods were experimented on road segmentation using three deep convolutional neural networks, UNet, DenseU-Net, and RoadVecNet. The color spaces included RGB, HSV, LAB, YCbCr, and YUV. The multitask learning methods adopted in this study involved utilizing multiple inputs, and multiple outputs. Multiple inputs were aerial images from the same area with different color spaces, and multiple outputs were road segmentation and road outline segmentation. As remote sensing data, National Land Survey of Finland's true orthophotos (from 2020), Massachusetts road imagery dataset, and Ottawa dataset were applied. Segmentation masks for National Land Survey of Finland's true orthophotos were extracted from Digiroad vectors with road width information. Road outline masks were generated from the segmentation masks. The studied neural networks were trained with the same data, learning rate, loss function, and optimizer for each color space, and pairs of color spaces. Multiple outputs were experimented with RGB color space. The comparative analysis assessed the performance of various neural networks across different color spaces using the F1-score metric. The experimental findings indicate that the choice of color space has little influence on the results of neural networks Deep learning methods can adapt to different color spaces well. In addition, the use of sharpening and edge enhancement augmentations had a slight effect on the results. [ABSTRACT FROM AUTHOR]