Abstract. This paper aims to discusses the extraction of urban features from airborne NISAR (NASA-ISRO SAR) data using deep learning algorithm for a part of Ahmedabad City. NISAR data is acquired in two wavelength bands (L and S) in hybrid polarization i.e., RH and RV. This study has used level two data viz., amplitude data. Pre-processing of NISAR data in L and S wavelength bands was carried out by using MIDAS, software developed and provided by the Space Applications Centre. Pre-processing viz., Speckle suppression using different filters in varying window sizes, radiometric and geometric calibration was performed. Variation of backscattering coefficient (Sigma- nought) in different wavelengths and polarizations for different land use features were analysed. NISAR data in conjunction with LISS 4 (5.8 m resolution) data is subjected to different fusion techniques. Qualitative and Quantitative analysis was carried out and Gram Schmidt technique was chosen for further analysis. Segmentation was performed to achieve better analysis of the fused image and the amplitude image. Lastly, a deep learning architecture was developed for the automatic classification of the image, and the Convolution Neural Network model was designed using mobile net and the regularization techniques. Deep learning architecture in conjunction with e-cognition developer was used for extracting urban features.

Abstract. Land cover mapping using remote-sensing imagery has attracted significant attention in recent years. Classification of land use and land cover is an advantage of remote sensing technology which provides all information about land surface. Numerous studies have investigated land cover classification using different broad array of sensors, resolution, feature selection, classifiers, Classification Techniques and other features of interest from over the past decade. One, Pixel based image classification technique is widely used in the world which works on their per pixel spectral reflectance. Classification algorithms such as parallelepiped, minimum distance, maximum likelihood, Mahalanobis distance are some of the classification algorithms used in this technique. Other, Object based image classification is one of the most adapted land cover classification technique in recent time which also considers other parameters such as shape, colour, smoothness, compactness etc. apart from the spectral reflectance of single pixel.At present, there is a possibility of getting the more accurate information about the land cover classification by using latest technology, recent and relevant algorithms according to our study. In this study a combination of pixel-by-pixel image classification and object based image classification is done using different platforms like ArcGIS and e-cognition, respectively. The aim of the study is to analyze LULC pattern using satellite imagery and GIS for the Ahmedabad district in the state of Gujarat, India using a LISS-IV imagery acquired from January to April, 2017. The over-all accuracy of the classified map is 84.48% with Producer’s and User’s accuracy as 89.26% and 84.47% respectively. Kappa statistics for the classified map are calculated as 0.84. This classified map at 1:10,000 scale generated using recent available high resolution space borne data is a valuable input for various research studies over the study area and also provide useful information to town planners and civic authorities. The developed technique can be replicated for generating such LULC maps for other study areas as well.

Though many individuals in the United States of America and worldwide identify as LGBTQ + (lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other identities), educational programs for allied health professions often do not adequately cover LGBTQ + issues. The literature clearly identifies a dearth of LGBTQ + information in undergraduate, graduate, and continuing education for allied health professionals. This lack of education and training causes real and perceived prejudice and discrimination by healthcare professionals against people who identify as LGBTQ +. Pertinent issues for people who identify as LGBTQ + and the language used to refer to these individuals changes over time so the LGBTQ + content that allied health education programs cover should be periodically reevaluated. This article summarizes the current state of education on LGBTQ + issues in allied health professions education and suggests contemporary LGBTQ + content that should be included in allied health professions education.

Abstract. The present study addresses the potential of airborne NASA – ISRO Synthetic Aperture Radar (NISAR) compact polarimetric (CP) data to discriminate the land cover classes emphasizing the urban area for parts of Ahmedabad city, India. This has been carried out by generating m-Delta, m-Chi and m-Alpha polarimetric decompositions using Compact Polarimetric L band NISAR data. In Hybrid Polarimetric data, both m-delta and m-chi decompositions have almost the same formulations, indicating that delta and chi play the same roles as indicators of single-bounce and double-bounce scattering. However, M-delta seem preferable over M-chi as stoke parameter delta is highly susceptible towards orientation. It is also observed that building orientation and density has effect on scattering pattern. This is attributed to the target orientation which is parallel to the look direction of the sensor. Supervised classification of m-Delta decomposition was carried out and over all accuracy of 81.1 % was observed in the study.

<p><strong>Abstract.</strong> Systematic inventory of glaciers is required for a variety of applications needed for the comprehensive development of the Himalayan region such as: a) disaster warning, b) estimation of irrigation potential, c) planning and operation of mini and micro hydroelectric power stations, etc.</p><p> A systematic inventory of the Himalayan glaciers at 1<span class="thinspace"></span>:<span class="thinspace"></span>50,000 scale was created for Indus, Ganga and Brahmaputra basins using Indian Remote Sensing Satellite data and attempted to modified global standards in GIS environment (Sharma et al, 2013). A robust, user- friendly web-based Himalayan Glacier Information System (HGIS), a first of its kind in the country is developed which facilitates any user to selectively display, query, analyse, compose maps and graphs and print, spatial and <i>aspatial</i> information on glaciers relevant to respective interests.</p><p> The HGIS architecture is based on Open Geospatial Consortium (OGC) standards and utilises OpenGeo Suite bundled software comprising of Postgresql (PostGIS), Geoserver, GeoWebCache and GeoExplorer each of those having a different function (Anonymous 2010). The spatial and aspatial glacier data sets were stored in a pre-defined format (Sharma et al, 2008) and imbibed into spatially enabled database (PostGIS), having sophisticated functions for spatial data analysis and query. For publishing the data on web page OGC-compliant services are used.</p><p> The HGIS information content comprise of a) glacier inventory maps and b) inventory data sheet. The map displays the glacier morphology features like accumulation zone and ablation (ice exposed and debris covered) zones, snout location, de-glaciated valleys, moraines and glacier lakes. The basin, sub-basin and administrative boundaries form the background. The inventory data sheet attributes for each glacier provides information on glacier Location, Identification, Dimension, Orientation, Elevation, Classification, etc. The spatial map and datasheet are linked by unique glacier identification number (galc_id) which is a key field present in all corresponding glacier related point, polygon or line layers. All the glacier attribute were made amenable to query and analysis by users. HGIS represents a significant step towards mapping and compiling individual glacier level inventory data in spatial form to fill the void in data and information on the status of Glaciers in the Himalaya and Trans-Himalayan Karakoram region. HGIS provides a basis for assessing the glacier inventory data which has applications in studies related to climate change, water resource planning, hydropower site selection and mitigation of glacial lake outburst flood (GLOF) hazards.</p>