ALS-derived raster visualization techniques have become common in recent years, opening up new possibilities for subtle landform detection in earth sciences and archaeology, but they have also introduced confusion for users. As a consequence, the choice between these visualization techniques is still mostly supported by empirical knowledge. Some attempts have been made to compare these techniques, but there is still a lack of analytical data. This work proposes a new method, based on gradient modelling and spatial statistics, to analytically assess the efficacy of these visualization techniques. A selected panel of outstanding visualization techniques was assessed first by a classic non-analytical approach, and secondly by the proposed new analytical approach. The comparison of results showed that the latter provided more detailed and objective data, not always consistent with previous empirical knowledge. These data allowed us to characterize with precision the terrain for which each visualization technique performs best. A combination of visualization techniques based on DEM manipulation (Slope and Local Relief Model) appeared to be the best choice for normal terrain morphometry, occasionally supported by illumination techniques such as Sky-View Factor or Negative Openness as a function of terrain characteristics.

International audienceThe use of Light Detection And Ranging (LiDAR) for archaeological purposes is becoming more prevalent in order to detect and to document remains located in forested areas. One of the main interests of airborne laser scanning is to put the archaeological information in their context, and to allow a better understanding of the relation between each item and its environment. This concept of archaeological landscape generally results in a too large amount of data to permit a manual analysis. This paper describes an approach for the automatic detection of elementary archaeological grazing structures, found in high concentration in some places of Auvergne (France). These elementary structures are generally connected, creating complex archaeological grazing sets. The detection process is based on the design of a model of an elementary grazing structure. The automatic detection is then carried out, based on the evaluation of the matching degree of each element with the model and on their belonging to complex archaeological grazing structures. The efficiency of the method is tested, by comparison with the manual digitalisation of an expert, on a restricted zone, and the results show that the success rate of the automatic detection reaches higher values than classical template matching approaches. The additional criterion, based on the belonging of each elementary structure to a more complex one, improves the detection success: In a complementary way, this approach offers new opportunities: it is also possible to detect complex structures with a template matching approach, if they contain some simple forms, that can be modelled

Acting as efficient earth‐movers, soldiers can be viewed as significant geomorphological drivers of landscape change when replaced in the recent debates on Anthropocene Geomorphology. ‘Polemoforms’, generated by military activities, correspond with a set of human‐made landforms of various sizes and geometries. They are particularly common on the World War One battlefield of Verdun (France) which ranks among the largest battles of attrition along the Western Front. The artillery bombardments and building of defensive positions in that battle significantly altered the landscape, resulting in thousands of shell craters, dugouts, and gun positions that have altered both the meso and microtopography. This paper proposes an innovative methodology to make an exhaustive inventory of these small‐scale conflict‐induced landforms (excluding linear features such as trenches) using a digital terrain model (DTM) acquired by airborne LiDAR on the whole battlefield. Morphometric analysis was conducted using Kohonen's self‐organizing maps (SOMs) and hierarchical agglomerative clustering (HAC) in order to quantify and classify the high number of war landforms. This combined approach allowed for mapping more than one million landforms which can be classified into eight different shapes including shell craters and various soldier‐made landforms (i.e. shelters, gun positions, etc.). Detection quality evaluation using field observations revealed the algorithm successfully classified 93% of shell craters and 74% of anthropologically constructed landforms. Finally, the iconographic database and map series produced will help archaeologists and foresters to better manage the historic site of Verdun, today covered by a large forest of ~10 000 ha. © 2019 John Wiley & Sons, Ltd.