AGRICULTURAL LAND CONVERSION IN THE TWIN CITIES: PART II, THE NATIONAL RESOURCES INVENTORY

We divided the thirteen-county Twin Cities Metropolitan Statistical Area into a core and a fringe of seven and six counties, respectively. The National Resources Inventory estimates that 170 thousand acres of the Core were converted from agriculture to other uses between 1982 and 1987, while only about 46 thousand acres of the Fringe were so converted. The conversion rate was much greater in the Core than on the Fringe according to the NRIbut not according to the Census of Agriculture. The number of acres of agricultural land converted for each new resident ranged from 0.15 in Sherburne County to 2.49 in Pierce County. Viewed another way, the increase in urban land to house new residents ranged from 0.28 in Ramsey County to 1.23 acres per person in Isanti County.Land Economics/Use,

Activity Recognition for Quality Assessment of Batting Shots in Cricket using a Hierarchical Representation

Quality assessment in cricket is a complex task that is performed by understanding the combination of individual activities a player is able to perform and by assessing how well these activities are performed. We present a framework for inexpensive and accessible, automated recognition of cricketing shots. By means of body-worn inertial measurement units, movements of batsmen are recorded, which are then analysed using a parallelised, hierarchical recognition system that automatically classifies relevant categories of shots as required for assessing batting quality. Our system then generates meaningful visualisations of key performance parameters, including feet positions, attack/defence, and distribution of shots around the ground. These visualisations are the basis for objective skill assessment thereby focusing on specific personal improvement points as identified through our system. We evaluated our framework through a deployment study where 6 players engaged in batting exercises. Based on the recorded movement data we could automatically identify 20 classes of unique batting shot components with an average F1-score greater than 88%. This analysis is the basis for our detailed analysis of our study participants’ skills. Our system has the potential to rival expensive vision-based systems but at a fraction of the cost

Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition

The ubiquitous availability of smartphones and smartwatches with integrated inertial measurement units (IMUs) enables straightforward capturing of human activities. For specific applications of sensor based human activity recognition (HAR), however, logistical challenges and burgeoning costs render especially the ground truth annotation of such data a difficult endeavor, resulting in limited scale and diversity of datasets. Transfer learning, i.e., leveraging publicly available labeled datasets to first learn useful representations that can then be fine-tuned using limited amounts of labeled data from a target domain, can alleviate some of the performance issues of contemporary HAR systems. Yet they can fail when the differences between source and target conditions are too large and/ or only few samples from a target application domain are available, each of which are typical challenges in real-world human activity recognition scenarios. In this paper, we present an approach for economic use of publicly available labeled HAR datasets for effective transfer learning. We introduce a novel transfer learning framework, Cross-Domain HAR, which follows the teacher-student self-training paradigm to more effectively recognize activities with very limited label information. It bridges conceptual gaps between source and target domains, including sensor locations and type of activities. Through our extensive experimental evaluation on a range of benchmark datasets, we demonstrate the effectiveness of our approach for practically relevant few shot activity recognition scenarios. We also present a detailed analysis into how the individual components of our framework affect downstream performance

Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition

Human activity recognition (HAR) in wearable computing is typically based on direct processing of sensor data. Sensor readings are translated into representations, either derived through dedicated preprocessing, or integrated into end-to-end learning. Independent of their origin, for the vast majority of contemporary HAR, those representations are typically continuous in nature. That has not always been the case. In the early days of HAR, discretization approaches have been explored - primarily motivated by the desire to minimize computational requirements, but also with a view on applications beyond mere recognition, such as, activity discovery, fingerprinting, or large-scale search. Those traditional discretization approaches, however, suffer from substantial loss in precision and resolution in the resulting representations with detrimental effects on downstream tasks. Times have changed and in this paper we propose a return to discretized representations. We adopt and apply recent advancements in Vector Quantization (VQ) to wearables applications, which enables us to directly learn a mapping between short spans of sensor data and a codebook of vectors, resulting in recognition performance that is generally on par with their contemporary, continuous counterparts - sometimes surpassing them. Therefore, this work presents a proof-of-concept for demonstrating how effective discrete representations can be derived, enabling applications beyond mere activity classification but also opening up the field to advanced tools for the analysis of symbolic sequences, as they are known, for example, from domains such as natural language processing. Based on an extensive experimental evaluation on a suite of wearables-based benchmark HAR tasks, we demonstrate the potential of our learned discretization scheme and discuss how discretized sensor data analysis can lead to substantial changes in HAR

German Antarctic Receiving Station (GARS) O'Higgins

In 2012, the German Antarctic Receiving Station (GARS) O'Higgins contributed to the IVS observing program with four observation sessions. Maintenance and upgrades were made, and a new replacement dewar is under construction in the observatory at Yebes, Spain