Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection

Discovering ancient agricultural terraces in desert regions is important for the monitoring of long-term climate changes on the Earth's surface. However, traditional ground surveys are both costly and limited in scale. With the increasing accessibility of aerial and satellite data, machine learning techniques bear large potential for the automatic detection and recognition of archaeological landscapes. In this paper, we propose a deep semantic model fusion method for ancient agricultural terrace detection. The input data includes aerial images and LiDAR generated terrain features in the Negev desert. Two deep semantic segmentation models, namely DeepLabv3+ and UNet, with EfficientNet backbone, are trained and fused to provide segmentation maps of ancient terraces and walls. The proposed method won the first prize in the International AI Archaeology Challenge. Codes are available at https://github.com/wangyi111/international-archaeology-ai-challenge

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Improved Gridded Precipitation Data Derived from Microwave Link Attenuation

The motivation for improving gridded precipitation data lies in weather now-casting and flood forecasting. Therefore, over the past decade, Commercial Microwave Link (CML) attenuation data have been used to determine rain rates between microwave antennas, and to produce more accurate countrywide precipitation grids. CML networks offer a unique advantage for precipitation measurements due to their high density. However, these data experience uncertainty from several sources as reported in earlier research. This current work determines the reliability of rainfall measurements for each link by comparing CML-derived rain rates to adjusted weather radar rainfall at the link location, over three months. Dynamic Time Warping (DTW) is applied to the pair of CML/radar time-series data in two study areas, Israel and Netherlands. Based on the DTW amplitude and temporal distance, unreliable links are identified and flagged, and interpolated gridded precipitation data are derived in each country after filtering out those unreliable links. Correlations between CML-derived grids and rain observations from an independent set of gauges, tested over several rain events in both study areas, are higher for the reliable subset of CML than the full set. For certain storm events, the Kendall rank correlation for the set of reliable CML is almost double that of the complete set, demonstrating that improved gridded precipitation data can be obtained by removing unreliable links

An evaluation of weather radar adjustment algorithms using synthetic data

Adjustment of weather radar estimates using observed precipitation has been an accepted procedure for decades. Ground observations of precipitation typically come from rain gauges, but can also include data from diverse networks of sensors, with different levels of reliability. This study presents a standardized framework for evaluating adjustment algorithms using synthetically constructed, but realistic, rain grids and weather radar rainfall. Ground observation points are randomly placed throughout the synthetic storm domain and the precipitation for each sensor is extracted from the true rain. Then a subset of the sensors are defined as unreliable, and a log-normal error factor is applied at those locations.This double network of rain sensors could be applicable, for example, when rainfall is derived from signal attenuation between commercial microwave link (CML) antennas. Past research has tested CML observations as a source of precipitation data and validated various radar adjustment algorithms. However, a comprehensive evaluation of adjustment algorithms using accurate gauge data mixed with CML observations at different densities is lacking.Five adjustment algorithms are applied to the synthetic radar grid: Mean Field Bias (MFB), a Multiplicative algorithm, Mixed (additive and multiplicative), Conditional Merge (CondMerge) and Kriging with External Drift (ICED). Generation of the synthetic framework, and application of the adjustment algorithms is repeated for 150 realizations. Comparison of coefficient of determination (R-2), root mean square error and linear regression for all adjustment procedures over all realizations indicates the following results. Only MFB and KED adjustments performed well when using accurate gauges. The kriging based KED was able to achieve good adjustment also with the addition of error-prone sensors. CondMerge and the Mixed and Multiplicative, however, resulted in poorer adjustments

Multispectral Approach for Identifying Invasive Plant Species Based on Flowering Phenology Characteristics

Invasive plant species (IPS) are the second biggest threat to biodiversity after habitat loss. Since the spatial extent of IPS is essential for managing the invaded ecosystem, the current study aims at identifying and mapping the aggressive IPS of Acacia salicina and Acacia saligna, to understand better the key factors influencing their distribution in the coastal plain of Israel. This goal was achieved by integrating airborne-derived hyperspectral imaging and multispectral earth observation for creating species distribution maps. Hyperspectral data, in conjunction with high spatial resolution species distribution maps, were used to train the multispectral images at the species level. We incorporated a series of statistical models to classify the IPS location and to recognize their distribution and density. We took advantage of the phenological flowering stages of Acacia trees, as obtained by the multispectral images, for the support vector machine classification procedure. The classification yielded an overall Kappa coefficient accuracy of 0.89. We studied the effect of various environmental and human factors on IPS density by using a random forest machine learning model, to understand the mechanisms underlying successful invasions, and to assess where IPS have a higher likelihood of occurring. This algorithm revealed that the high density of Acacia most closely related to elevation, temperature pattern, and distances from rivers, settlements, and roads. Our results demonstrate how the integration of remote-sensing data with different data sources can assist in determining IPS proliferation and provide detailed geographic information for conservation and management efforts to prevent their future spread

Spatial and spectral analysis of fairy circles in Namibia on a landscape scale using satellite image processing and machine learning analysis

Fairy circles (FCs) are a unique phenomenon characterized by circular patches, 4–10 m in diameter, of bare soil within a vegetated matrix. This project aimed to study the spatial and spectral characteristics of FCs on a landscape scale in Namibia. The specific objectives of this research are (1) processing satellite observations to explore the FCs distributions by applying statistical analysis and deep machine learning algorithms; (2) analyzing the FCs' geometric attributes to retrieve their spatial patterns regarding topographic features nearby. The FCs were classified within 25 km2 by processing 15 input layers through a convolutional neural network (CNN) model. The layers include four WorldView2 spectral bands, derived vegetation, biocrust, and mineral indices, and textural characteristics. The FCs’ geometry was extracted, and spatial autocorrelation was performed. By labeling 1600 FCs and using the CNN model, 14,536 FCs were mapped with 0.97% accuracy and a binary cross-entropy loss function value of only 0.01. Field measurements and laboratory analysis justified the need to use spectral indices for the model. Unique elongated FCs, clustered by hotspot analysis, were quantified and mapped along watercourses in alluvial fans with notable connectivity. On a landscape scale that has not yet been studied, spatial and spectral analyses became possible only with valuable remote sensing retrievals, deep statistical analysis, and machine learning algorithms

Applying spatial analysis of genetic and environmental data to predict connection corridors to the New World screwworm populations in South America

The myiasis causing New World screwworm (NWS) fly is responsible for substantial losses to livestock breeders in the Americas. Due to the negative impact of the NWS fly in animal health, expansion of successful NWS fly eradication programmes is under discussion. However, the effects of geography and environmental diversity on NWS population structure and migration patterns need to be assessed before any political decision is made to implement such a programme. We present a GIS tool to construct potential connection corridors among sampling localities based on genetic and environmental data. We integrate, through a home-made python script, a friction raster based on a Maxent niche model and the pairwise Phi(ST) statistic. Among 38 NWS fly sampling localities from South America, we find a high population connectivity among the sampling localities from the south of the Amazon region. The region along the Atlantic Ocean was identified as the most probable migration corridor between the north (NAG) and the south (SAG) of the Amazon region. The approach highlighted previously undetected population structure within NAG showing low to medium connectivity through the Andes, correlating with current understanding of NWS fly migration in South America. Also, the approach is flexible, allowing future research to incorporate other niche simulations and genetic differentiation metrics. With this flexibility, the tool could become part of any AW-IPM by helping to target regions for control. (C) International Atomic Energy Agency 2014. Published by Elsevier B.V. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Study site and <i>A</i>. <i>tortilis</i> distributions.

<p><b>a</b>: map of subpopulation sampling site distribution in Israel and Jordan. Also shown are the major drainage basins across the study area. <b>b</b>: acacia gazelles (<i>Gazella gazella acaciae</i> = <i>G</i>.<i>g</i>. <i>cora</i>) feeding on the foliage of <i>Acacia tortilis</i> at Yotveta Nature Reserve, Israel. Photo: Benny Shalmon. <b>c</b>: the location of the two sites from the species’ central distribution in Sudan and Egypt (black trees; map adapted from <a href="http://www.bjdesign.com" target="_blank">www.bjdesign.com</a>). <b>d</b>: the approximate distribution of <i>A</i>. <i>tortilis</i>. This map is similar but not identical to a map published by FAO (<a href="http://www.fao.org/docrep/006/Q2934E/Q2934E05.htm" target="_blank">http://www.fao.org/docrep/006/Q2934E/Q2934E05.htm</a>), and is therefore for illustrative purposes only.</p

Detecting hierarchical levels of connectivity in a population of <i>Acacia tortilis</i> at the northern edge of the species’ global distribution: Combining classical population genetics and network analyses

<div><p>Genetic diversity and structure of populations at the edge of the species’ spatial distribution are important for potential adaptation to environmental changes and consequently, for the long-term survival of the species. Here, we combined classical population genetic methods with newly developed network analyses to gain complementary insights into the genetic structure and diversity of <i>Acacia tortilis</i>, a keystone desert tree, at the northern edge of its global distribution, where the population is under threat from climatic, ecological, and anthropogenic changes. We sampled <i>A</i>. <i>tortilis</i> from 14 sites along the Dead Sea region and the Arava Valley in Israel and in Jordan. In addition, we obtained samples from Egypt and Sudan, the hypothesized origin of the species. Samples from all sites were genotyped using six polymorphic microsatellite loci.Our results indicate a significant genetic structure in <i>A</i>. <i>tortilis</i> along the Arava Valley. This was detected at different hierarchical levels—from the basic unit of the subpopulation, corresponding to groups of trees within ephemeral rivers (wadis), to groups of subpopulations (<i>communities</i>) that are genetically more connected relative to others. The latter structure mostly corresponds to the partition of the major drainage basins in the area. Network analyses, combined with classical methods, allowed for the identification of key <i>A</i>. <i>tortilis</i> subpopulations in this region, characterized by their relatively high level of genetic diversity and centrality in maintaining gene flow in the population. Characterizing such key subpopulations may enable conservation managers to focus their efforts on certain subpopulations that might be particularly important for the population’s long-term persistence, thus contributing to species conservation within its peripheral range.</p></div