Population mapping in informal settlements with high-resolution satellite imagery and equitable ground-truth

We propose a generalizable framework for the population estimation of dense, informal settlements in low-income urban areas–so called ’slums’–using high-resolution satellite imagery. Precise population estimates are a crucial factor for efficient resource allocations by government authorities and NGO’s, for instance in medical emergencies. We utilize equitable ground-truth data, which is gathered in collaboration with local communities: Through training and community mapping, the local population contributes their unique domain knowledge, while also maintaining agency over their data. This practice allows us to avoid carrying forward potential biases into the modeling pipeline, which might arise from a less rigorous ground-truthing approach. We contextualize our approach in respect to the ongoing discussion within the machine learning community, aiming to make real-world machine learning applications more inclusive, fair and accountable. Because of the resource intensive ground-truth generation process, our training data is limited. We propose a gridded population estimation model, enabling flexible and customizable spatial resolutions. We test our pipeline on three experimental site in Nigeria, utilizing pre-trained and fine-tune vision networks to overcome data sparsity. Our findings highlight the difficulties of transferring common benchmark models to real-world tasks. We discuss this and propose steps forward

Management alternatives for urea use in corn and wheat production

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (February 9, 2007)Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2006.Dissertations, Academic -- University of Missouri--Columbia -- Agronomy.Traditionally, urea has been incorporated to avoid losses of N by ammonia volatilization. However, this option is not available when topdressing wheat. The objective of this project is to evaluate several strategies designed to reduce the risk of ammonia volatilization loss from urea topdress applied on wheat. The tested strategies included treating urea with Agrotain (a urease inhibitor) or Agrotain + dicyandiamide (DCD), and use of coated urea products. Fertilizers were applied at a rate of 80 kg N ha-1. In 2004, wheat yields were low and none of the strategies designed to reduce N loss resulted in higher wheat yields with 95% confidence. However, the weather was favorable for ammonia volatilization and there was evidence from both yield and reflectance that urea + Agrotain + DCD was more effective than urea in delivering N to the crop. In 2005, urea + Agrotain, urea + Agrotain + DCD, and ammonium nitrateproduced higher yields when compared with broadcast urea. The addition of a timing effect for the 2005 experiment resulted in a significant and large yield response when treatments were applied in March compared to in January. Application of polymer- and gel-coated urea did not improve wheat yield relative to urea in either year. Agrotain + DCD was the most effective treatment for increasing yield and profitability from urea over the two study years

A framework of quality assessment methods for crowdsourced geographic information : a systematic literature review

Collaboration is the foundation to strengthen disaster preparedness and for effective emergency response actions at all levels. Some studies have highlighted that remote volunteers, i.e., volunteers supported by Web 2.0 technologies, possess the potential to strengthen humanitarian relief organizations by offering information regarding disaster-affected people and infrastructure. Although studies have explored various aspects of this topic, none of those provided an overview of the state-of-the-art of researches on the collaboration among humanitarian organizations and communities of remote volunteers. With the aim of overcoming this gap, a systematic literature review was conducted on the existing research works. Therefore, the main contribution of this work lies in examining the state of research in this field and in identifying potential research gaps. The results show that most of the research works addresses the general domain of disaster management, whereas only few of them address the domain of humanitarian logistics. Collaboration among Humanitarian Relief Organizations and Volunteer Technical Communities: Identifying Research Opportunities and Challenges through a Systematic Literature Review (PDF Download Available). Available from: https://www.researchgate.net/publication/315790817_Collaboration_among_Humanitarian_Relief_Organizations_and_Volunteer_Technical_Communities_Identifying_Research_Opportunities_and_Challenges_through_a_Systematic_Literature_Review [accessed May 26, 2017]

Determining flooded areas using crowd sensing data and weather radar precipitation : a case study in Brazil

Crowd sensing data (also known as crowdsourcing) are of great significance to support flood risk management. With the growing volume of available data in the past few years, researchers have used in situ sensor data to filter and prioritize volunteers’ information. Nevertheless, stationary, in situ sensors are only capable of monitoring a limited region, and this could hamper proper decision-making. This study investigates the use of weather radar precipitation to support the processing of crowd sensing data with the goal of improving situation awareness in a disaster and early warnings (e.g., floods). Results from a case study carried out in the city of São Paulo, Brazil, demonstrate that weather radar data are able to validate flooded areas identified from clusters of crowd sensing data. In this manner, crowd sensing and weather radar data together can not only help engage citizens, but also generate high-quality data at finer spatial and temporal resolutions to improve the decision-making related to weather-related disaster events

Road distance and travel time for an improved house price Kriging predictor

The paper designs an automated valuation model to predict the price of residential property in Coventry, United Kingdom, and achieves this by means of geostatistical Kriging, a popularly employed distance-based learning method. Unlike traditional applications of distance-based learning, this papers implements non-Euclidean distance metrics by approximating road distance, travel time and a linear combination of both, which this paper hypothesizes to be more related to house prices than straight-line (Euclidean) distance. Given that – to undertake Kriging – a valid variogram must be produced, this paper exploits the conforming properties of the Minkowski distance function to approximate a road distance and travel time metric. A least squares approach is put forth for variogram parameter selection and an ordinary Kriging predictor is implemented for interpolation. The predictor is then validated with 10-fold cross-validation and a spatially aware checkerboard hold out method against the almost exclusively employed, Euclidean metric. Given a comparison of results for each distance metric, this paper witnesses a goodness of fit (r²) result of 0.6901 ± 0.18 SD for real estate price prediction compared to the traditional (Euclidean) approach obtaining a suboptimal r² value of 0.66 ± 0.21 SD

Exploiting hydrogen bonding to direct supramolecular polymerization at the air/water interface

Fluid interfaces provide an advanced platform for directed self-assembly of organic composites and formation of supramolecular polymers (SPs). Intermolecular interactions govern the supramolecular polymerization processes, with hydrogen bonding (H-bonding) as a key interaction in supramolecular chemistry and biology. Two purposefully designed supra-amphiphiles for assessing the role of H-bonding were designed and their supramolecular polymerization (SP) at the air/water interface was compared. H-bonding was confirmed by in situ experimental and computational techniques as the required intermolecular interaction for attaining SPs with well-defined molecular arrangement. Control of H-bonding as opposite to traditionally considered interactions, e.g., π-π stacking is proposed as a successful strategy for SP at fluid interfaces