Time stability of asymmetric Fabry-Perot modulator based analog lightwave links

Lightwave links for analog signal transfer are being developed and evaluated for application in high-density interconnects. The reflective links are based on compact electro-optic intensity modulators connected by ribbons of single-mode fibres to remotely located transceivers (lasers and photoreceivers) and read-out electronics. For long-term characterization, four Asymmetric Fabry-Perot Modulator (AFPM) prototypes were continuously operated and monitored over a period of eight monthes. The collected data allow evaluation of the system time stability and simulation of the possible recalibration procedures. The recalibration requirements to achieve the desirable accuracy and reliability are inferred statistically

UAV IMAGES AND DEEP-LEARNING ALGORITHMS FOR DETECTING FLAVESCENCE DOREE DISEASE IN GRAPEVINE ORCHARDS

Abstract. One of the major challenges in precision viticulture in Europe is the detection and mapping of flavescence dorée (FD) grapevine disease to monitor and contain its spread. The lack of effective cures and the need for sustainable preventive measures are nowadays crucial issues. Insecticides and the plants uprooting are commonly employed to withhold disease infection, even if these solutions imply serious economic consequences and a strong environmental impact. The development of a rapid strategy to identify the disease is required to cover large portions of the crop and thus to limit damages in a time-effective way. This paper investigates the use of Unmanned Aerial Vehicles (UAVs), a cost-effective approach to early detection of diseased areas. We address this task with an object detection deep network, Faster R-CNN, instead of a traditional pixel-wise classifier. This work tests Faster R-CNN performance on this specific application through a comparative analysis with a pixel-wise classification algorithm (Random Forest). To take advantage of the full image resolution, the experimental analysis is performed using the original UAV imagery acquired in real conditions (instead of the derived orthomosaic). The first result of this paper is the definition of a new dataset for FD disease identification by UAV original imagery at the canopy scale. Moreover, we demonstrate the feasibility of applying Faster-R-CNN as a quasi-real-time alternative solution to semantic segmentation. The trained Faster-R-CNN achieved an average precision of 82% on the test set

CHANGE DETECTION BETWEEN DIGITAL SURFACE MODELS FROM AIRBORNE LASER SCANNING AND DENSE IMAGE MATCHING USING CONVOLUTIONAL NEURAL NETWORKS

Airborne photogrammetry and airborne laser scanning are two commonly used technologies used for topographical data acquisition at the city level. Change detection between airborne laser scanning data and photogrammetric data is challenging since the two point clouds show different characteristics. After comparing the two types of point clouds, this paper proposes a feed-forward Convolutional Neural Network (CNN) to detect building changes between them. The motivation from an application point of view is that the multimodal point clouds might be available for different epochs. Our method contains three steps: First, the point clouds and orthoimages are converted to raster images. Second, square patches are cropped from raster images and then fed into CNN for change detection. Finally, the original change map is post-processed with a simple connected component analysis. Experimental results show that the patch-based recall rate reaches 0.8146 and the precision rate reaches 0.7632. Object-based evaluation shows that 74 out of 86 building changes are correctly detected

Yield stress, heterogeneities and activated processes in soft glassy materials

The rheological behavior of soft glassy materials basically results from the interplay between shearing forces and an intrinsic slow dynamics. This competition can be described by a microscopic theory, which can be viewed as a nonequilibrium schematic mode-coupling theory. This statistical mechanics approach to rheology results in a series of detailed theoretical predictions, some of which still awaiting for their experimental verification. We present new, preliminary, results about the description of yield stress, flow heterogeneities and activated processes within this theoretical framework.Comment: Paper presented at "III Workshop on Non Equilibrium Phenomena...", Pisa 22-27 Sep. 200

Supervised methods of image segmentation accuracy assessment in land cover mapping

Land cover mapping via image classification is sometimes realized through object-based image analysis. Objects are typically constructed by partitioning imagery into spatially contiguous groups of pixels through image segmentation and used as the basic spatial unit of analysis. As it is typically desirable to know the accuracy with which the objects have been delimited prior to undertaking the classification, numerous methods have been used for accuracy assessment. This paper reviews the state-of-the-art of image segmentation accuracy assessment in land cover mapping applications. First the literature published in three major remote sensing journals during 2014–2015 is reviewed to provide an overview of the field. This revealed that qualitative assessment based on visual interpretation was a widely-used method, but a range of quantitative approaches is available. In particular, the empirical discrepancy or supervised methods that use reference data for assessment are thoroughly reviewed as they were the most frequently used approach in the literature surveyed. Supervised methods are grouped into two main categories, geometric and non-geometric, and are translated here to a common notation which enables them to be coherently and unambiguously described. Some key considerations on method selection for land cover mapping applications are provided, and some research needs are discussed

Towards Uncovering Socio-Economic Inequalities Using VHR Satellite Images and Deep Learning

In many cities of the Global South, informal and deprived neighborhoods, also commonly called slums, continue to proliferate, but their locations and dwellers' socio-economic status are often invisible in official statistics and maps. Very high resolution (VHR) satellite images coupled with deep learning allow us to efficiently map these areas and study their socio-economic and spatio-temporal variability to support interventions. This paper investigates a deep transfer learning approach based on convolutional neural networks (CNN) to identify the socio-economic variability of poor neighborhoods in Bangalore, India. Our deep network, pre-trained on a slum classification data set, is tuned towards the prediction of a continuous-valued socio-economic index capturing multiple levels of deprivation. Experimental results show that the CNN-based regression model can explain the socio-economic variability with an R2 of 0.75. The use of additional publicly available geographic information layers allow us to spatially extend the analysis beyond the surveyed deprived area data samples to uncover city-wide patterns of socio-economic inequalities

The temporal dynamics of slums employing a CNN-based change detection approach

Along with rapid urbanization, the growth and persistence of slums is a global challenge. While remote sensing imagery is increasingly used for producing slum maps, only a few studies have analyzed their temporal dynamics. This study explores the potential of fully convolutional networks (FCNs) to analyze the temporal dynamics of small clusters of temporary slums using very high resolution (VHR) imagery in Bangalore, India. The study develops two approaches based on FCNs. The first approach uses a post-classification change detection, and the second trains FCNs to directly classify the dynamics of slums. For both approaches, the performances of 3 × 3 kernels and 5 × 5 kernels of the networks were compared. While classification results of individual years exhibit a relatively high F1-score (3 × 3 kernel) of 88.4% on average, the change accuracies are lower. The post-classification results obtained an F1-score of 53.8% and the change-detection networks obtained an F1-score of 53.7%. According to the trajectory error matrix (TEM), the post-classification results scored higher for the overall accuracy but lower for the accuracy difference of change trajectories than the change-detection networks. Although the two methods did not have significant differences in terms of accuracy, the change-detection network was less noisy. Within our study area, the areas of slums show a small overall decrease; the annual growth of slums (between 2012 and 2016) was 7173 m2, in contrast to an annual decline of 8390 m2. However, these numbers hid the spatial dynamics, which were much larger. Interestingly, areas where slums disappeared commonly changed into green areas, not into built-up areas. The proposed change-detection network provides a robust map of the locations of changes with lower confidence about the exact boundaries. This shows the potential of FCNs for detecting the dynamics of slums in VHR imagery