Mapping solar array location, size, and capacity using deep learning and overhead imagery

The effective integration of distributed solar photovoltaic (PV) arrays into existing power grids will require access to high quality data; the location, power capacity, and energy generation of individual solar PV installations. Unfortunately, existing methods for obtaining this data are limited in their spatial resolution and completeness. We propose a general framework for accurately and cheaply mapping individual PV arrays, and their capacities, over large geographic areas. At the core of this approach is a deep learning algorithm called SolarMapper - which we make publicly available - that can automatically map PV arrays in high resolution overhead imagery. We estimate the performance of SolarMapper on a large dataset of overhead imagery across three US cities in California. We also describe a procedure for deploying SolarMapper to new geographic regions, so that it can be utilized by others. We demonstrate the effectiveness of the proposed deployment procedure by using it to map solar arrays across the entire US state of Connecticut (CT). Using these results, we demonstrate that we achieve highly accurate estimates of total installed PV capacity within each of CT's 168 municipal regions

Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery

Modern deep neural networks (DNNs) are highly accurate on many recognition tasks for overhead (e.g., satellite) imagery. However, visual domain shifts (e.g., statistical changes due to geography, sensor, or atmospheric conditions) remain a challenge, causing the accuracy of DNNs to degrade substantially and unpredictably when testing on new sets of imagery. In this work, we model domain shifts caused by variations in imaging hardware, lighting, and other conditions as non-linear pixel-wise transformations, and we perform a systematic study indicating that modern DNNs can become largely robust to these types of transformations, if provided with appropriate training data augmentation. In general, however, we do not know the transformation between two sets of imagery. To overcome this, we propose a fast real-time unsupervised training augmentation technique, termed randomized histogram matching (RHM). We conduct experiments with two large benchmark datasets for building segmentation and find that despite its simplicity, RHM consistently yields similar or superior performance compared to state-of-the-art unsupervised domain adaptation approaches, while being significantly simpler and more computationally efficient. RHM also offers substantially better performance than other comparably simple approaches that are widely used for overhead imagery.Comment: Includes a main paper (10 pages). This paper is currently undergoing peer revie

Temporal dynamics of genetic clines of invasive European green crab (Carcinus maenas) in eastern North America

Evolutionary Applications published by John Wiley & Sons Ltd. Reproduced with the permission of the Minister of Fisheries and Oceans Canada. Two genetically distinct lineages of European green crabs (Carcinus maenas) were independently introduced to eastern North America, the first in the early 19th century and the second in the late 20th century. These lineages first came into secondary contact in southeastern Nova Scotia, Canada (NS), where they hybridized, producing latitudinal genetic clines. Previous studies have documented a persistent southward shift in the clines of different marker types, consistent with existing dispersal and recruitment pathways. We evaluated current clinal structure by quantifying the distribution of lineages and fine-scale hybridization patterns across the eastern North American range (25 locations, ~39 to 49°N) using informative single nucleotide polymorphisms (SNPs; n = 96). In addition, temporal changes in the genetic clines were evaluated using mitochondrial DNA and microsatellite loci (n = 9–11) over a 15-year period (2000–2015). Clinal structure was consistent with prior work demonstrating the existence of both northern and southern lineages with a hybrid zone occurring between southern New Brunswick (NB) and southern NS. Extensive later generation hybrids were detected in this region and in southeastern Newfoundland. Temporal genetic analysis confirmed the southward progression of clines over time; however, the rate of this progression was slower than predicted by forecasting models, and current clines for all marker types deviated significantly from these predictions. Our results suggest that neutral and selective processes contribute to cline dynamics, and ultimately, highlight how selection, hybridization, and dispersal can collectively influence invasion success

Affimer proteins are versatile and renewable affinity reagents

Molecular recognition reagents are key tools for understanding biological processes and are used universally by scientists to study protein expression, localisation and interactions. Antibodies remain the most widely used of such reagents and many show excellent performance, although some are poorly characterised or have stability or batch variability issues, supporting the use of alternative binding proteins as complementary reagents for many applications. Here we report on the use of Affimer proteins as research reagents. We selected 12 diverse molecular targets for Affimer selection to exemplify their use in common molecular and cellular applications including the (a) selection against various target molecules; (b) modulation of protein function in vitro and in vivo; (c) labelling of tumour antigens in mouse models; and (d) use in affinity fluorescence and super-resolution microscopy. This work shows that Affimer proteins, as is the case for other alternative binding scaffolds, represent complementary affinity reagents to antibodies for various molecular and cell biology applications

All's well that begins Wells: Celebrating 60 years of Animal Behaviour and 36 years of research on anuran social behaviour

The scientific study of frogs and toads as important systems in behavioural ecology traces its roots to an influential review published in this journal 36 years ago (Wells 1977a, ‘The social behaviour of anuran amphibians’, Animal Behaviour, 25, 666–693). In just 28 pages, Wells summarized the state of knowledge on important behaviours associated with anuran breeding and introduced an evolutionary framework ‘for understanding the relationship between social behaviour and ecology’ (page 666) that was largely lacking in earlier treatments of this group. Not only is Wells's review one of the most cited papers ever published in Animal Behaviour, it is also responsible for setting broad research agendas and shaping much of our current thinking on social behaviour in an entire order of vertebrates. As such, it is entirely appropriate that we honour Wells's review and its contributions to the study of animal behaviour in this inaugural essay celebrating 12 papers selected by the community as the most influential papers published in the 60-year history of Animal Behaviour. In our essay, we place Wells's review in historical context at the dawn of behavioural ecology, highlight the field's progress in answering some major research questions outlined in the review, and provide our own prospectus for future research on the social behaviour of anuran amphibians. Highlights ► This essay celebrates Kent Wells's (1977, Animal Behaviour, 25, 666–693) paper, ‘The social behaviour of anuran amphibians’. ► We place the article in historical context and outline its major contributions. ► We discuss progress on anuran social behaviour since its publication in 1977. ► We provide our own prospectus on the future of anuran behavioural ecology