This report summarizes the results from the three year research project 3D-Lidar for Long Distances from 2023 to 2025. This work was funded by the Swedish Armed Forces R&D research programme for sensors and low observables (FoT SoS, AT.9220425). The project has studied the utility of 3D-imaging lidar for threat detection at long ranges in a coastal environment, including 3D-data analysis methods that can provide enhanced capability in military systems. The report also describes other activities within the research area of imaging laser sensors, collaboration with with research organizations, and the dissemination of knowledge to the Swedish Armed Forces, FMV and other stakeholders. Data from a 3D-imaging lidar enables measuring of geometries and physical size of objects in a scene, that can be used e.g. for automatic target detection and recognition algorithms. Further, the technique can image partially occluded objects behind e.g. camouflage or vegetation. The project has performed field trials in a coastal environment with a photon counting lidar system developed at FOI. During the project, the system has been further developed to enable continous measurements at ranges up to 6 km with range resolution at decimetre scale. Natural objects, e.g. tree trunks, and objects placed in the scene, i.e. tents, reference boards, or persons can be seen using visual inspection of the 3D-data. Detection and recognition with photon counting lidar data, using automatic or manual methods, is affected by background light and atmospheric conditions such as visibility and turbulence. The project has conducted a statistical analysis of visibility conditions along the Swedish coast. During the lidar measurements, it was mostly good visibility and low turbulence. Hence, a detailed analysis of the lidar performance has not been possible to obtain for conditions with reduced visibility. In darkness, it is possible to collect data of high quality, while the background light during daytime requires lowering the sensitivity of the lidar receiver. The objective for future work is to study how the performance is affected by turbulence and reduced visibility, as well as studies of how the performance can be improved in daylight conditions. Automated analysis methods can be used to process and analyse large quantities of complex data and make them accessible to an operator. The research results show that methods for change detection and machine learning can be adapted to defence applications for target detection, target recognition, and mapping. Change detection is an important analysis tool that does not require a known target signature. Analysis methods for change detection from a fixed sensor position has shown to be effective for detection in 3D-lidar data from long ranges. Another result is that analysis methods based on machine learning can provide good results in detection of persons at long distances. Methods for detailed terrain classification using the Swedish national lidar data from aerial measurements performes well and should be further developed in different applications to take advantage of the opportunities.