Global change of land use systems : IMAGE: a new land allocation module

The Integrated Model to Assess the Global Environment (IMAGE) aims at assessing the state of the environment taking into account the effects of human activities. Although human population often makes use of a land area to satisfy various needs, most of the current global land use datasets and models use a classification based on dominant land use/cover types disregarding the diversity and intensity of human activities. In this working document we investigate if the simulation of land use change and the IMAGE outcomes can be improved by using a classification based on land use systems. An expert based cluster analysis was used to identify and map land use systems. The analysis accounted for population density, accessibility, land use / cover types and livestock and provided a new insight on human interactions with the environment. Then, a conceptual framework was developed and implemented to simulate land use systems changes based on local conditions and demand for agricultural products and accounting for land management changes

The impact of knowledge management processes on organisational performance

Copyright @ 2012 ISEing.In today's increasingly competitive business environment, the use of knowledge to gain a competitive advantage has become a serious concern for all organisations. However, despite the increasing number of studies relating to Knowledge Management (KM) in developed countries, few studies have explored this issue within the context of developing countries. Moreover, some industries have been affected more acutely than others in the transition to a knowledge-based economy. Towards covering this gap, this study aims at investigating the impact of Knowledge Management processes on Organisational Performance (OP). In this paper, the authors propose a conceptual model through an in-depth investigation of the previous and current studies in the area of Knowledge Management and Organisational Performance. Through an extensive classification of Knowledge Management processes, the proposed model explores the impact of each Knowledge Management process on improving the level of Organisational Performance. It is envisaged that this model can play a role in guiding the process of Knowledge Management implementation in order to maximise the beneficial effects of Knowledge Management processes on Organisational Performance

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models trained on large annotated datasets. Unfortunately, creating large agricultural datasets with pixel-level annotations is an extremely time consuming task, actually penalizing the usage of data-driven techniques. In this paper, we face this problem by proposing a novel and effective approach that aims to dramatically minimize the human intervention needed to train the detection and classification algorithms. The idea is to procedurally generate large synthetic training datasets randomizing the key features of the target environment (i.e., crop and weed species, type of soil, light conditions). More specifically, by tuning these model parameters, and exploiting a few real-world textures, it is possible to render a large amount of realistic views of an artificial agricultural scenario with no effort. The generated data can be directly used to train the model or to supplement real-world images. We validate the proposed methodology by using as testbed a modern deep learning based image segmentation architecture. We compare the classification results obtained using both real and synthetic images as training data. The reported results confirm the effectiveness and the potentiality of our approach.Comment: To appear in IEEE/RSJ IROS 201

Land-use classification and mapping using landsat imagery for GIS database in Langkawi Island

This study examined the land use changes in Langkawi Island for over 12 years. Land use maps were derived by analysing temporally available satellite imageries of that area. Subsequent Landsat imageries of the years 2006, 2014 and 2018 were processed in Environment for Visualising Images (ENVI) software using Normalize Differences Built-Up Index (NDBI) and supervised classification. The land use classes obtained were categorised according to the Soil and Water Assessment Tool (SWAT) land use classification namely URLD, URMD, URHD, FRSE, UINS, UIDU, URTN, RICE, AGRR, and WATR. The analysis of the land use maps provides a comparison for the area of land use class around Langkawi Island based on the Rancangan Kawasan Khas 2020 (RKK). This study will give an overview of the stakeholder on the current land use of Langkawi Island for future land use planning. Moreover, the land use map generated in this study can be used as a functional land use input for the SWAT model and provide a temporal Geographic Information System (GIS) database on the land use of the Langkawi Island