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The spatial and temporal distribution of Alpine snow is a sensitive gauge of environmental change. While understanding past snow dynamics is essential for reconstructing past climate and forecasting future trends, reliable snowpack data prior to the instrumental record are scarce. We present a novel pairing of cosmogenic paleothermometry and luminescence photochronometry which constrain the temperature and insolation history of bedrock outcrops at the Gotthard Pass, Switzerland, over the last ∼15,000 years. By coupling these results with cosmogenic 14C‐10Be chronology and modern in situ rock thermometry, we infer a ∼70‐day reduction of snowpack at the topographic mid‐slope. Our data indicate stable environmental conditions throughout the Holocene, followed by a 6.6 ± 2.9°C increase of ground surface temperature, coeval with an order‐of‐magnitude or more increase in ground surface insolation. Bracketing the onset of these changes between 1504 and 1807 CE, our findings tie the snowpack decline with the onset of human industrialization. Plain Language Summary: The extent of snow cover is shrinking in high elevation mountain environments due to climate change. However, it is challenging to determine when snow cover began to change because humans have only been monitoring snow cover for several decades, and snow cover has appeared to shrink over this entire time. We used two new geologic records of ground temperature and light exposure—both of which are impacted by the duration and amount of snow cover—to assess when snow cover began to change in the Gotthard Pass in Switzerland. We found that the middle slopes of the pass began experiencing snow cover loss three centuries ago, well before historical monitoring of snow cover in this region. This record of prolonged snow cover loss matters for the informed management of mountain water sources, rock and snow avalanche risk, and ecosystem change. Key Points: We present a novel data set of cosmogenic paleothermometry and luminescence photochronometry at the Gotthard Pass, SwitzerlandStable Holocene environmental conditions are followed by increases in ground temperature and insolation caused by snow cover declineThese environmental changes began between 1504 and 1807 CE, tying the observed snowpack decline with the onset of human industrialization [ABSTRACT FROM AUTHOR]

Recent years have seen a proliferation of new digital products for the efficient management of indoor spaces, with important applications like emergency management, virtual property showcasing and interior design. While highly innovative and effective, these products rely on accurate 3D models of the environments considered, including information on both architectural and non‐permanent elements. These models must be created from measured data such as RGB‐D images or 3D point clouds, whose capture and consolidation involves lengthy data workflows. This strongly limits the rate at which 3D models can be produced, preventing the adoption of many digital services for indoor space management. We provide a radical alternative to such data‐intensive procedures by presenting Walk2Map, a data‐driven approach to generate floor plans only from trajectories of a person walking inside the rooms. Thanks to recent advances in data‐driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer‐level smartphones, which allows for an effortless and scalable mapping of real‐world indoor spaces. Our work is based on learning the latent relation between an indoor walk trajectory and the information represented in a floor plan: interior space footprint, portals, and furniture. We distinguish between recovering area‐related (interior footprint, furniture) and wall‐related (doors) information and use two different neural architectures for the two tasks: an image‐based Encoder‐Decoder and a Graph Convolutional Network, respectively. We train our networks using scanned 3D indoor models and apply them in a cascaded fashion on an indoor walk trajectory at inference time. We perform a qualitative and quantitative evaluation using both trajectories simulated from scanned models of interiors and measured, real‐world trajectories, and compare against a baseline method for image‐to‐image translation. The experiments confirm that our technique is viable and allows recovering reliable floor plans from minimal walk trajectory data. [ABSTRACT FROM AUTHOR]