Currently, analyzing satellite images requires an unsustainable amount of manual labor. Semiautomatic solutions for land-cover classification of satellite images entail the incorporation of expert knowledge. To increase the scalability of the built solutions, methods that automate image processing and analysis pipelines are required. Recently, deep learning (DL) models have been applied to challenging vision problems with great success. We expect that the use of DL models will soon outperform shallow networks and other classification algorithms, as recently achieved in multiple domains. Here, we consider the task of land-cover classification of satellite images. This seems particularly appropriate for deep classifiers due to the combined high dimensionality of the data with the presence of compositional dependencies between pixels, which can be used to characterize a particular class. We develop a pipeline for analyzing satellite images using a deep convolutional neural network for practical applications. We present its successful application for land-cover classification, where it achieves 86% classification accuracy on unseen raw images.
Corine land cover 2000 (CLC2000) is a project jointly managed by the Joint Research Centre (JRC) and the European
Environment Agency (EEA). Its aim is to update the Corine land cover database in Europe for the year 2000. Landsat-7
Enhanced Thematic Mapper (ETM) satellite images were used for the update and were acquired within the framework of
the Image2000 project. Knowledge of the land status through the use of mapping CORINE Land Cover is of great
importance to study of interaction land cover and land use categories in Europe scale. This paper presents the accuracy
assessment methodology designed and implemented to validate the Iberian Coast CORINE Land Cover 2000
cartography. It presents an implementation of a new methodological concept for land cover data production, Object-
Based classification, and automatic generalization to assess the thematic accuracy of CLC2000 by means of an
independent data source based on the comparison of the land cover database with reference data derived from visual
interpretation of high resolution satellite imageries for sample areas. In our case study, the existing Object-Based
classifications are supported with digital maps and attribute databases. According to the quality tests performed, we
computed the overall accuracy, and Kappa Coefficient.
We will focus on the development of a methodology based on classification and generalization analysis for built-up areas
that may improve the investigation. This study can be divided in these fundamental steps:
-Extract artificial areas from land use Classifications based on Land-sat and Spot images.
-Manuel interpretation for high resolution of multispectral images.
-Determine the homogeneity of artificial areas by generalization process.
-Overall accuracy, Kappa Coefficient and Special grid (fishnet) test for quality test.
Finally, this paper will concentrate to illustrate the precise accuracy of CORINE dataset based on the above general
Urban morphology "implies 'form,' 'land use,' and 'density,' and has connotations with the shape, structure, pattern and
organization of land use, and the system of relation between them" (Donnay, Barnsley, and Longley, 2001). It reflects
the combination of complex special artificial areas such as buildings, roads, parks, gardens and even ecological systems
of soil and water. To understand the dynamics and patterns of urban extend related with their interactions in
heterogeneous landscapes, the spatial complexity needs to be quantified accurately, depending upon the morphological
analysis and their relation with territory. Morphological analysis, which refers to the geometric characteristics of urban
sites, illustrates its usefulness in determining the analogies between patterns of cities and their "physical" characters
providing indicators of the aspect of settlement form and structure. Remote sensing might be helpful on the regional
scale in evaluating the role that landscape play in connecting different settlements within urban regions and in separating
the core city from the surrounding countryside. It used to map urban morphology of human settlements and monitoring
urban growth (Batty and Longley, 1987). The information produced by remote sensing is spatially referenced through an
implicit geometric location of the pixels. Various urban forms are potentially discernible using such devices, including
linear objects (Sohn and Bowman, 2001). The aim of this paper is to classify, evaluate and compare different urban
forms related to street networks and land characters, also considering the morphological typologies of urban settlements
by moving from the spatial scale of a municipality to a wider territorial. The intent is to discover secure principals to find
the most likely urban models of cities, taking topographical parameters into account. This research carried out focusing
upon the metropolitan region of Barcelona, with urban sites defined according to the contiguity of artificial and
administrative boundaries. The TeleAtlas and land activity classification deriving from Spot Imagery form the basis of
this study. We will focus on the development of a methodology to classify the geometric properties and intrinsic space of
urban settlements based on their characteristics and fundamental forms.