Lowland Maya civilization flourished in the tropical region of the Yucatan peninsula and environs for more than 2500 years (~1000 BCE to 1500 CE). Known for its sophistication in writing, art, architecture, astronomy, and mathematics, Maya civilization still poses questions about the nature of its cities and surrounding populations because of its location in an inaccessible forest. In 2016, an aerial lidar survey across 2144 square kilometers of northern Guatemala mapped natural terrain and archaeological features over several distinct areas. We present results from these data, revealing interconnected urban settlement and landscapes with extensive infrastructural development. Studied through a joint international effort of interdisciplinary teams sharing protocols, this lidar survey compels a reevaluation of Maya demography, agriculture, and political economy and suggests future avenues of field research.

No abstract

Collectivization of agriculture (1950s-1970s) was one of the most important periods in landscape development in Slovakia. Traditionally managed agricultural landscapes, that covered more than half of the Slovak territory, were transformed into large-scale fields and only fragments of traditional agricultural landscapes survived. We mapped the remaining traditional agricultural landscapes using aerial photos and historical maps. We then statistically analyzed the various geographical factors and their influence on the transformation process of traditional and collectivized fields, i.e., slope steepness, soil fertility, distance from settlements and isolation from regional capital cities. The comparison was performed using classification tree analysis. We constructed a set of decision rules that explain why fields were managed traditionally or collectivized. Our findings show that traditional agricultural fields were more likely to persist on steep terrain, less fertile soils, and on locations that were closer to the settlements, but more isolated from the regional capital cities. Steepness played the most important role: small-scale fields located on steep areas were not accessible to heavy machinery and therefore, frequently survived the collectivization. We show that the selected geographical factors are good explanatory variables for the collectivization of arable fields and orchards. For vineyards and grasslands, however, the explanatory power of the selected geographical factors is lower, and we suspect that other factors, not depicted in the analysis play an important role. Keywords Land-use change Á Classification tree analysis Á Driving forces Á Post-socialist countries Á Resilience Á Cultural landscape

Central and Eastern Europe has experienced fundamental land use changes since the collapse of socialism around 1990. We analyzeanalyzed the patterns and determinants of agricultural land abandonment and recultivation in Slovakia during the transition from a state-controlled economy to an open-market economy (1986 to 2000) and the subsequent accession to the European Union (2000 to 2010). We quantified agricultural land-use change based on available maps derived from 30-m multi-seasonal Landsat imagery and analyzeanalyzed the socioeconomic and biophysical determinants of the observed agricultural land-use changes using boosted regression trees. We used a scenario-based approach to assess future agricultural land abandonment and recultivation until 2060. The maps of agricultural land use analysis reveal that cropland abandonment was the dominant land use process on 11% of agricultural land from 1986 to 2000, and on 6% of the agricultural land from 2000 to 2010. Recultivation occurred on approximately 2% of agricultural land in both periods. Although most abandoned land was located in the plains, the rate of abandonment was twice as high in the mountainous landscapes. The likelihood of abandonment increased with increased distance from the national capital (Bratislava), decreased with an increase of annual mean temperatures and was higher in proximity to forest edges and on steeper slopes. Recultivation was largely determined by the opposite effects. The scenario for 2060 suggests that future agricultural land abandonment and recultivation may largely be determined by climate and terrain conditions and, to a lesser extent, by proximity to economic centers. Our study underscores the value of synergetic use of satellite data and land-use modeling to provide the input for land planning, and to anticipate the potential effects of changing environmental and policy conditions.

Airborne LiDAR produced large amounts of data for archaeological research over the past decade. Labeling this type of archaeological data is a tedious process. We used a data set from Pacunam LiDAR Initiative survey of lowland Maya region in Guatemala. The data set contains ancient Maya structures that were manually labeled, and ground verified to a large extent. We have built and compared two deep learning-based models, U-Net and Mask R-CNN, for semantic segmentation. The segmentation models were used in two tasks: identification of areas of ancient construction activity, and identification of the remnants of ancient Maya buildings. The U-Net based model performed better in both tasks and was capable of correctly identifying 60–66% of all objects, and 74–81% of medium sized objects. The quality of the resulting prediction was evaluated using a variety of quantifiers. Furthermore, we discuss the problems of re-purposing the archaeological style labeling for production of valid machine learning training sets. Ultimately, we outline the value of these models for archaeological research and present the road map to produce a useful decision support system for recognition of ancient objects in LiDAR data.