This project evaluates the suitability of 3D interior space models acquired using Apple’s RoomPlan API for daylight simulations. The main contribution is a Python-based tool that converts RoomPlan output into HoneyBeeJSON by automatically reconstructing the ceiling and adding window frames, which are missing in RoomPlan’s output. Although RoomPlan is also able to capture furniture, these elements were not used in the geometric evaluation or in the daylight simulations. The resulting models can be directly used in Grasshopper for daylight simulations, reducing the modeling time required by practitioners. To assess the suitability of RoomPlan, three office interiors were scanned using a TLS and modeled both manually and with an iPhone 12 Pro.
The manual models were used as ground truth. For each room, a geometric evaluation and a daylight simulation evaluation were performed using three model versions: manual, RoomPlan with extruded window frames, and RoomPlan without extruded window frames. For both the geometrical and the daylight performance evaluation, it is apparent that the windows' frames extrusion is significant to achieve more accurate results. Geometric accuracy was evaluated using Chamfer and Hausdorff distances, showing good overall accuracy. However, errors were observed in wall heights when the ceiling was not clearly visible and in the separation of windows located close to each other. The models were used for point-in-time grid-based illuminance and view-based luminance simulations in Grasshopper using Honeybee.
For the illuminance simulations, the MAE is approximately 269 lux and the MAPE is 19.5%. For DGP, the MAPE is 7.6% for the RoomPlan models with extruded window frames, with only one misclassification of the DGP category. The results indicate that RoomPlan can be used for visual comfort studies but not for daylight availability studies. Despite these results, suggestions for further work are given, considering both the geometrical and the daylight simulation performance evaluation of the RoomPlan models.

The present report is the end result of the project that was carried out as part of the Geomatics Synthesis Project in cooperation with AllMaps, an open-source platform dedicated to the viewing and georeferencing of historic maps. The main objective of the project was to automatically georeference historic map series curated and digitised by the Dutch National Archive. This was based on the  corner coordinates of the map sheets. The first issue that had to be tackled was the reprojection of the original coordinates which were in Bonne projection
to WGS84 coordinates. To determine the corners of the map content within the sheets two methods were implemented. The first one detects the lines based on HoughLines Probabilistic Transformation and the second one detects lines based on the distribution of black pixels in the rows and columns of the images. In addition to map sheets with corner coordinates, there are two other sets of images which were georeferenced utilising a convolution neural network that performs feature matching. The feature matching was performed by running the two sets of images against the georeferenced sheets with known corner coordinates. To minimise the search space for this process a geocoder was used to determine the approximate location of the image. The implemented methods appear to hold the potential for georeferencing old map series. It is worth noting that the developed algorithms, while effective in many cases, may encounter challenges when dealing with irregularities on map sheets caused by the passage
of time, such as damage. Consequently, there is a great opportunity to further enhance the algorithms to ensure they can consistently and accurately georeference images, even when faced with such irregularities. This ongoing development will lead to improved georeferencing accuracy and user confidence.