The new land cover dataset will allow mapping of natural resources, human settlements and human activities in South Sudan and within neighboring countries. It will represent the most innovative and updated dataset developed for South Sudan, integrating high-resolution multi-temporal imagery, object-based image analysis

The habitat of tsetse fly (Glossina spp.) depends upon climatic conditions, host availability and land cover characteristics. In this paper, the Land Cover Classification System (LCCS), developed by the Food and Agriculture Organization (FAO) and the United Nations Environment Programme (UNEP), is proposed as a tool to harmonize land cover mapping exercises carried out in the context of tsetse and trypanosomiasis (T&T) research and control.

The National Forestry Authority has monitored Uganda’s land cover, including forested areas, periodically since 1990. The land cover classification is comprised of 13 classes as shown in the table below. The first five classes in the table refer to the different types of forests in Uganda. The largest forest type is woodland. Compared to other landcover types, forests are a small proportion of the country area.

Vegetation mapping provides basic information for forest management and planning. In remote sensing research, the process of creating an accurate vegetation map is an important subject. Recently, there has been growing research interest in the object-oriented image classification techniques. The object-oriented image classification consists of multi-dimensional features including object features and thus requires multi-dimensional image classification approaches.

The prospect of GIS technology to create and to use of soil maps is stated. Conducted a GIS analysis of soil cover the territory of Cherven District showed the predominance of retisols of large areas isomorphic and asymmetrical shapes, three times less than the average area for podzols with isomorphic form. The most complicated form of areas marked for alluvial soil types. Their coefficients of irregularity boundaries typically less than 0.2, and are fluvial forms of areas.

AW3Dsup(TM), the world's most precise global 3D map service, became the world's first five-meter-resolution 3D map covering all global land spaces in April 2016 by using Advanced Land Observing Satellite (ALOS). In addition to five-meter-resolution global map, enhanced service offers a higher-resolution 3D map at half-meter or two-meter resolution, both of which are offered on an on-demand basis using commercial high-resolution satellite imagery. This paper introduces the project history, technical characteristics, service contents, use cases and future prospects of AW3Dsup(TM).

In this study, we compared pixel-based image analysis and object-based image analysis (OBIA) as methods of land cover classification of urban areas, using high resolution digital aerial photography. The study area was Setagaya Ward, Tokyo, Japan, and we carried out supervised classification using aerial photographs with 25-cm spatial resolution, and with both visible bands and a near infrared band. The overall accuracy of the object-based classification was approximately 6 to 20 percentage points higher than that of the pixel-based classification.

Application of the satellite remote sensing techniques to wildlife research began from discernment of the individual animal and/or evaluation of animal behavior from the photography experiments. Satellite remote sensing to wildlife research at the present has applied for the purpose of evaluating the animal habitat. Trends in satellite remote sensing for wildlife are evaluating the index of wildlife habitat and estimating relationship with an environmental variables and animal distribution.

Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing methods used, among others. This study extracted agricultural fields in the former homelands of Venda and Gazankulu in Limpopo Province, South Africa.

An Introduction to Machine Learning

Machine learning is an application of artificial intelligence (AI) that enables systems to programmatically “learn” and improve from past experience. Computers use algorithms and statistical models to “learn” patterns and insights from sample sets of data (often called “training data sets”), and apply those insights to make intelligent predictions and decisions about much larger sets of data.