Landscape reshaping processes have an immense effect on humans, being a fundamental component of their habitat. The morphological signatures they leave on the terrain enable us to trace them, understand their nature, develop strategies to avert hazards and provide a more sustainable future. As such entities are better traced in their natural 3-D shape, the last decade has seen an expedite growth in the use of high-resolution laser scanning (LiDAR) technologies to document, monitor, and analyse them. Nonetheless, the unorganised nature, span, and massive data volume, turn the interaction with the acquired data cumbersome and difficult. Hence, common practices are rooted in manual feature delineation or in use of off-the-shelf raster-based tools, which were developed for different applications and scales. The outcome is subjective and prone to misidentifications or distortions. As there is an evident gap between the richness of the data and geoscientists practices, I propose in MorE3D to develop a new processing framework that strengthens that link. This will enable to highlight features, provide quantitative morphometric information, and facilitate analysis of trends and patterns which are essential to asses natural processes. Based on the understanding of the geometric signature recorded in the data, I propose to formulate an energy function that uses global optimisation to detect entities. Furthermore, as many applications combine active and passive sensors (lasers and cameras, for example) or use drone-based imaging to supplement the data during acquisition, I will to further extend the proposed scheme and develop a unified multichannel optimisation framework, where all acquired information is integrated and utilised. Such models will open new avenues to analyse features, detect patterns, trace changes, and essentially enable accurate measurements of the processes affecting landforms, while paving the way to the relevant research communities.