The Compass 

About: The Compass is an academic journal. The journal publishes majorly in the area(s): Population & Social media. It has an ISSN identifier of 1043-7150. Over the lifetime, 245 publications have been published receiving 940 citations.

Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data

Abstract: Accurate prediction of crop yields in developing countries in advance of harvest time is central to preventing famine, improving food security, and sustainable development of agriculture. Existing techniques are expensive and difficult to scale as they require locally collected survey data. Approaches utilizing remotely sensed data, such as satellite imagery, potentially provide a cheap, equally effective alternative. Our work shows promising results in predicting soybean crop yields in Argentina using deep learning techniques. We also achieve satisfactory results with a transfer learning approach to predict Brazil soybean harvests with a smaller amount of data. The motivation for transfer learning is that the success of deep learning models is largely dependent on abundant ground truth training data. Successful crop yield prediction with deep learning in regions with little training data relies on the ability to fine-tune pre-trained models.

AirSim-W: A Simulation Environment for Wildlife Conservation with UAVs

Abstract: Increases in poaching levels have led to the use of unmanned aerial vehicles (UAVs or drones) to count animals, locate animals in parks, and even find poachers. Finding poachers is often done at night through the use of long wave thermal infrared cameras mounted on these UAVs. Unfortunately, monitoring the live video stream from the conservation UAVs all night is an arduous task. In order to assist in this monitoring task, new techniques in computer vision have been developed. This work is based on a dataset which took approximately six months to label. However, further improvement in detection and future testing of autonomous flight require not only more labeled training data, but also an environment where algorithms can be safely tested. In order to meet both goals efficiently, we present AirSim-W, a simulation environment that has been designed specifically for the domain of wildlife conservation. This includes (i) creation of an African savanna environment in Unreal Engine, (ii) integration of a new thermal infrared model based on radiometry, (iii) API code expansions to follow objects of interest or fly in zig-zag patterns to generate simulated training data, and (iv) demonstrated detection improvement using simulated data generated by AirSim-W. With these additional simulation features, AirSim-W will be directly useful for wildlife conservation research.

Examining Security and Privacy Research in Developing Regions

Abstract: Prior research suggests that security and privacy needs of users in developing regions are different than those in developed regions. To better understand the underlying differentiating factors, we conducted a systematic review of Human-Computer Interaction for Development and Security & Privacy publications in 15 proceedings, such as CHI, SOUPS, ICTD, and DEV, from the past ten years. Through an in-depth analysis of 114 publications that discuss security and privacy needs of people in developing regions, we identified five key factors---culture, knowledge gaps, unintended technology use, context, and usability and cost considerations---that shape security and privacy preferences of people in developing regions. We discuss how these factors influence their security and privacy considerations using case studies on phone sharing and surveillance. We then present a set of design recommendations and research directions for addressing security and privacy needs of people in resource-constrained settings.

A Machine Learning Approach to Modeling Human Migration

Abstract: Human migration is a type of human mobility, where a trip involves a person moving with the intention of changing their home location. Predicting human migration as accurately as possible is important in city planning applications, international trade, spread of infectious diseases, conservation planning, and public policy development. Traditional human mobility models, such as gravity models or the more recent radiation model, predict human mobility flows based on population and distance features only. These models have been validated on commuting flows, a different type of human mobility, and are mainly used in modeling scenarios where large amounts of prior ground truth mobility data are not available. One downside of these models is that they have a fixed form and are therefore not able to capture more complicated migration dynamics. We propose machine learning models that are able to incorporate any number of exogenous features, to predict origin/destination human migration flows. Our machine learning models outperform traditional human mobility models on a variety of evaluation metrics, both in the task of predicting migrations between US counties as well as international migrations. In general, predictive machine learning models of human migration will provide a flexible base with which to model human migration under different what-if conditions, such as potential sea level rise or population growth scenarios.

Care and the Practice of Data Science for Social Good

Abstract: Data science is an interdisciplinary field that extracts insights from data through a multi-stage process of data collection, analysis and use. When data science is applied for social good, a variety of stakeholders are introduced to the process with an intention to inform policies or programs to improve well-being. Our goal in this paper is to propose an orientation to care in the practice of data science for social good. When applied to data science, a logic of care can improve the data science process and reveal outcomes of "good" throughout. Consideration of care in practice has its origins in Science and Technology Studies (STS) and has recently been applied by Human Computer Interaction (HCI) researchers to understand technology repair and use in under-served environments as well as care in remote health monitoring. We bring care to the practice of data science through a detailed examination of our engaged research with a community group that uses data as a strategy to advocate for permanently affordable housing. We identify opportunities and experiences of care throughout the stages of the data science process. We bring greater detail to the notion of human-centered systems for data science and begin to describe what these look like.