Karlstads kommun innehar en så kallad solkarta vars syfte är att ge kommunens invånare en översikt över hur mycket solenergi som infaller på varje individuell takyta och kan användas som underlag för beslut om installation av solpaneler på byggnadstak. Kartan är interaktiv och sträcker sig över hela kommunen. Tyvärr brister den i detaljnivå utanför Karlstad tätort och skulle behöva en uppdatering. Syftet med studien är att undersöka metoder att utvinna tvådimensionella takplansytor enligt LOD2 utifrån byggnadsfotavtryck och punktmolnsdata, som sedan ska kunna lägga grunden till en interaktiv solkarta. Tre metoder för takplanssegmentering valdes ut som baserades på tre olika GIS-mjukvaror: ArcGIS Pro, Whitebox Tools och TerraScan. Studieområdet, beläget på industriområdet Våxnäs i Karlstad, bestod av 68 byggnader med varierande taktyper av olika hög komplexitet. En ytterligare dimension till studien var att två olika indatamängder jämfördes för varje segmenteringsmetod: ett högupplöst fotogrammetriskt framställt punktmoln utifrån bilder tagna med UAV (Unmanned Aerial Vehicle) samt ett lägre upplöst punktmoln insamlat med flygburen laserskanning. Totalt erhölls sex olika resultat som utvärderades efter fullständighet och utseende. Den högsta medelfullständigheten för varje metod var: 99,6 % för metoden baserad på TerraScan, 90,2 % för metoden baserad på Whitebox Tools och 82,0 % för metoden baserad på ArcGIS Pro. Gällande indatamängder gav UAV-datamängden ca 6 procentenheter bättre medelfullständighet för de två bästa metoderna och 10 procentenheter lägre för metoden som funkade sämst. Gällande användbarheten av resultaten är kontentan att TerraScan-metoden hade lagt en bra grund för en solkarta. Whitebox Tools-metoden hade sannolikt också kunnat vara användbar om en förbättrad generaliseringsalgoritm i efterbearbetningen hade applicerats. Studien diskuterar skillnader, felkällor samt nämner några ytterligare beprövade metoder som aldrig färdigställdes på grund av odugliga resultat. Problem som återstår att lösa är hantering av hål i punktmolnsdata inför takplanssegmentering. / Karlstad Municipality has what is known as a solar radiation map with the purpose of providing an overview of how much solar irradiance that is received by individual roof planes. It serves as a basis for decisions regarding the installation of solar panels on building roofs. The map is interactive and covers the entire municipality. Unfortunately, it lacks detail outside the city of Karlstad and would benefit from an update. The study aims to explore methods for extracting two-dimensional roof planes according to LOD2 (Level of Detail 2) using building footprints and point cloud data. The roof planes could later form the foundation for an interactive solar map. Three methods for roof segmentation were examined, each based on different software: ArcGIS Pro, Whitebox Tools, and TerraScan. The study area, located in the Våxnäs industrial area in Karlstad consisted of 68 buildings with varying roof types and complexities. An additional dimension to the study involved comparing two different input datasets for each segmentation method: a high-resolution photogrammetric point cloud generated from UAV (Unmanned Aerial Vehicle) images and a lower-resolution point cloud collected with airborne laser scanning. In total, six different results were evaluated based on completeness and appearance. The highest mean completeness for each method was: 99,6 % for the TerraScan-based method, 90,2 % for the Whitebox Tools-based method and 82,0 % for the ArcGIS Pro-based method. Regarding input datasets, the two best methods showed an increase of approximately 6 percentage points in mean completeness for the UAV dataset, while the least effective method showed a decrease of 10 percentage points. In terms of practicality, the TerraScan method provided a solid basis for a solar map. The Whitebox Tools method could most likely be usable if a better generalization algorithm in post-processing is cultivated. The study also discusses differences, potential sources of error, and mentions some additional methods that were not fully developed due to inadequate results. Remaining challenges include addressing gaps of missing data in point clouds before roof plane segmentation.

This diploma thesis deals with the classification of the point clouds taken by different carrier and with various density. The terrain model and building models were created from provided data sets. Also the software equipment is described. Achieved outcomes of elaboration are presented in each corresponding chapter. In conclusion, the overall evaluation and assessment of the results of processing is done.

The New National Elevation Model (NNH) is a new high-resolution digital elevation model (DEM) of Sweden from airborne laser scanning. It creates many new opportunities, particularly in the area of flood mapping. NNH is provided by Lantmäteriet in two formats, both in raw LIDAR (Light Detection and Ranging) data and in grid format with two meter resolution. These alternatives have advantages and disadvantages and the aim of this thesis research is to identify these. One of the focuses of the study is data storage and thus data structure analysis, resolution and storage facilities. The research questions are: Why and in what context the different NNH-products from the National Land Survey are used (DEM 2+ or point cloud)? What constraints and opportunities are created by the different options, mainly in terms of different software, resolution, and data storage? The study area is Kärsön in Ekerö municipality located in Stockholms län and has an approximate area of 25 square kilometers. The study is divided into two parts. The first objective is to identify the consequences of using different software to create DEM from pointcloud compared to the DEM2+ model. Height models with a two meter resolution are created in FME and ArcGIS. The models are then compared with the grid from Lantmäteriet, created in TerraScan. The second objective is to examine the impact of the change in resolution, both the storage aspect and both the accuracy aspect. Inverse Distance Weighted (IDW) is an interpolation method which in previous studies proved to have the best results on high resolution LIDAR data. This model was tested and compared with a model from FMEs built-in function and the model from Lantmäteriet wich are based on triangulation (also proved a good method in previous studies).  The grid created in TerraScan has good properties such as accuracy. The results show that the built-in ArcGIS model is not sufficient for the purposes of the model. Flood mapping requires continuous surfaces and the model lacks large areas of data. However, there are other aspects such as the break lines, these cannot be added to the TerraScan model or in the IDW but in the FME-modeler it is possible. In addition, it is not possible to edit the model that is delivered from Lantmäteriet. If there are outliers in the data, they will have much impact on the result. With a model created from the point cloud it is easy to remove these outliers. Increased resolution gives a quadratic increase in storage space so it is considered important not to use a resolution that is not really necessary.  If the purpose of the analysis requires higher resolution than two meters it is possible to achieve higher accuracy for areas with high point density. The raw data format also provides opportunities to create additional models with other uses, building models or forest inventory application can for example be extracted from the data. If the purpose is that the finished grid model is adequate, there is no direct reason to spend time creating a new model. But for a user with knowledge of laser data structure and processing, creating elevation models from raw LIDAR data could give advantages.