Land cover and forest segmentation using deep neural networks

Tiivistelmä. Land Use and Land Cover (LULC) information is important for a variety of applications notably ones related to forestry. The segmentation of remotely sensed images has attracted various research subjects. However this is no easy task, with various challenges to face including the complexity of satellite images, the difficulty to get hold of them, and lack of ready datasets. It has become clear that trying to classify on multiple classes requires more elaborate methods such as Deep Learning (DL). Deep Neural Networks (DNNs) have a promising potential to be a good candidate for the task. However DNNs require a huge amount of data to train including the Ground Truth (GT) data. In this thesis a DL pixel-based approach backed by the state of the art semantic segmentation methods is followed to tackle the problem of LULC mapping. The DNN used is based on DeepLabv3 network with an encoder-decoder architecture. To tackle the issue of lack of data the Sentinel-2 satellite whose data is provided for free by Copernicus was used with the GT mapping from Corine Land Cover (CLC) provided by Copernicus and modified by Tyke to a higher resolution. From the multispectral images in Sentinel-2 Red Green Blue (RGB), and Near Infra Red (NIR) channels were extracted, the 4th channel being extremely useful in the detection of vegetation. This ended up achieving quite good accuracy on a DNN based on ResNet-50 which was calculated using the Mean Intersection over Union (MIoU) metric reaching 0.53MIoU. It was possible to use this data to transfer the learning to a data from Pleiades-1 satellite with much better resolution, Very High Resolution (VHR) in fact. The results were excellent especially when compared on training right away on that data reaching an accuracy of 0.98 and 0.85MIoU

Shariah Screening Methodologies: SAC-SC Vs DJIM Comparative Study and Impact Assessment on Their Performance

Purpose – The purpose of this paper is to compare between two screening methodologies in terms of applied methods and the impact assessment. Other than this the paper measures the performance of screened stocks in terms of risk & return and compare it to the conventional onesDesign/methodology/approach – The methods used are a combination of archival and bibliographic research based on some previously published articles. Also the papers use secondary data from published reports.Findings – The paper identifies the impact assessment of the screening methodologies and how investors will not sacrifice part of their returns in order to achieve their moral and ethical values.Originality/value – Many studies compared the two screening indices in term of methodologies; however this paper investigates and uses a quantitative analysis on the impact and performance of the screening methodologies. Furthermore it compares the stages of screening between Shariah Advisory Council of Securities commission (SAC-SC) and Dow Jones Islamic Market Index (DJIM)Keywords – screening, methodologies, impact assessmentPaper type – comparative case stud