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19

I would have to say that the most complete software environment for Machine Learning and nonparametric modeling is R. This is a big field in statistics, spanning K-NN, Kernel smoothing, General Additive Models, weak learners, support vectors, neural nets, semi-parametric spline regression, imputation, etc... I would highly recommend reading: Hastie, T., R. ...


12

You can use GRASS GIS for this which supports texture extraction and image classification based on a radiometric/segmentation approach. For an idea, check this conference abstract, a planned talk at the Geoinformatics FCE CTU 2011. See also: http://grass.osgeo.org/wiki/Image_processing and http://grass.osgeo.org/wiki/Image_classification for an overview.


10

I'd strongly recommend scikits-learn for Python. It supports supervised and unsupervised classification and the documentation is excellent (particularly check out the Machine Learning for Astronomical Data Analysis tutorial and the accompanying YouTube video (note: this is 3 hours long)). The project is under active development, with the last version being ...


7

I poked around on the State of Hawaii GIS site but most of the layers were too general or too old. The NOAA Coastal Services Center has some Landsat ETM derived land-cover data for Hawaii. There's also some 2.4m Quickbird-derived land-cover data for all islands except the Big Island. Hope that helps!


7

A good overview of machine learning techniques in R is the machine learning taskview. It offers a host of different algorithms, recommended by the experts.


6

To simplify the raster it might be worth looking at gdal_sieve, it's available under the "Raster" menu. See: http://www.gdal.org/gdal_sieve.html N.


5

It does vary by Country - Turkey is poor because the data they used is minimal. For France, Germany, UK, Ireland the data accuracy is vastly better. If you want the accepted paper on the project "The Corine Land Cover (CLC2000) database received a thumbs up for accuracy from an assessment of the project, details of which were released by the EEA today. ...


5

Creating landuse database, you need sensor technologies to detect and classify objects. i think you already know that: There are two main types of remote sensing: passive remote sensing and active remote sensing. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding areas. Reflected sunlight is the most ...


5

Split tools has an error in ArcGIS 10 SP2. The tool makes the split, but leaves all features empty. Esri was registered this error and recommends for now, if you want use the split tool, downgrade ArcGIS to SP1. I suggest you visit this link http://resources.arcgis.com/es/gallery/file/geoprocessing/details?entryID=6C5D9A77-1422-2418-7F6C-01564409B1AF , where ...


5

The only high resolution global land cover that I know is the one done by the PR of China. In Europe they seemed to use CORINE as an ancillary data, so it is difficult to judge the consistency accross the world, but it has a spatial resolution of 30 m. http://www.globallandcover.com/GLC30Download/index.aspx http://unstats.un.org/unsd/default.htm You ...


5

There is a considerable body of literature on individual crown detection in spectral and lidar data. Methods wise, perhaps start with: Falkowski, M.J., A.M.S. Smith, P.E. Gessler, A.T. Hudak, L.A. Vierling and J.S. Evans. (2008). The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. ...


5

Yes, there are free object-oriented (segmentation) software available. A few that come to mind are Spring, ITK, Orfeo toolbox and GRASS GIS. I would however point out that image segmentation is a poor direction to peruse when trying to model fractional cover. A segmentation algorithm is designed to minimize within unit variance and maximize between unit ...


4

If I understand you correctly, you are looking for a supervised classification procedure. Some theoretical background: http://rst.gsfc.nasa.gov/Sect1/Sect1_17.html This is certainly possible through grass: http://grass.osgeo.org/wiki/Image_classification#Supervised_classification_2 As an alternative you could also look at saga (I'm not saying it is better, ...


4

there are lots of method for calculating land use change in gis systems. if you want to use Arcgis, you should check out Confusion Matrix Analysis for your work. with confusion matrix you can also measure urban sprawl, too.. it is of course necessary to do some research. Wikipedia defination: In the field of artificial intelligence, a confusion matrix ...


4

To generalize, try running a majority filter. This is available in saga (and grass as well, check markusN his answer). An explanation for how it works from arcgis: http://edndoc.esri.com/arcobjects/9.2/net/shared/geoprocessing/spatial_analyst_tools/majority_filter.htm


4

Your question assumes that machine learning algorithms for land classification are somehow distinct from software used for other machine learning applications. There are some applications that require special treatment because of unusual characteristics, but there is no reason I know of to think that land use needs special treatment. If land use data can ...


4

I am assuming that by mode you mean the most frequent class? You can use the R function "table" to calculate the frequencies of a vector. x <- c(1,1,2,3,4,4,4,4) table(x) Then use which.max to return the class associated with the most frequent class. To return the actual class name you need to wrap the statement in names. which.max( table(x) ) names( ...


4

It is a difficult thing that you are attempting. Small subtle changes in reflectances caused by different acquisition dates will cause major errors to arise when using your approach. You will have to do more preprocessing of your data, in order to have your approach be reliable. Normalizing the other years to your reference will most likely help, but it may ...


4

I would look at the support of you individual classes. If support for a given class is marginal in your fit model, the error may propagate in very undesirable ways. I would also consider fitting a series of binary models and predicting probabilities of each class separately. You could then perform a sensitivity test on the probabilities and evaluate if ...


4

There are three main reasons to flag a pixel as NoData in a classified image : 1) No input data : remote sensing dat could be missing for several reasons, including cloud, cloud shadow, temporary snow cover, darkness, sensor dysfunctionning, 2) Insufficient information : there was a valid value for the pixel, but not enough information to classify it. ...


4

You can make a paletted raster by assigning a colortable in the legend. If you have a raster called r and a data frame like yours above called ctab, with value and red/green/blue colour values, you can do something like this: > ctable = rep(NA,max(ctab$value)+1) > ctable[ctab$value+1] = rgb(ctab$red,ctab$green,ctab$blue,maxColorValue=255) > ...


3

you can use gdal_polygonize.py for converting raster to vector, if u previously use . some information is here. produces a polygon feature layer from a raster SYNOPSIS gdal_polygonize.py [-o name=value] [-nomask] [-mask filename] raster_file [-b band] [-q] [-f ogr_format] out_file [layer] [fieldname] beside this in qgis ...


3

You can first use the "mode" operator of r.neighbors in GRASS GIS (via Sextante plugin), then vectorize with r.to.vect to obtain polygons. Perhaps the "mode" operator should be run more than one time.


3

As Aragon correctly points out, there are many options for detecting changes in land use using GIS. Before starting, it's worth being aware of the differences between land USE and land COVER, see here for a helpful explanation (it's certainly a distinction that you'll want to make in your dissertation). In addition, bare in mind that any data that you ...


3

There is a group out of Duke University that have developed some interesting script tools for ArcGIS, including random forest models. Marine Geospatial Ecology Tools


3

Did you have a look at eCognition? With their new Version (8.9) they provide Random Forests algorithm within a GUI environment. You can create nice process trees and include object features.


3

Within the US you can obtain detailed soil data from NRCS. For vegetation, check out the GAP data.


3

Slight correction to a previous answer... NOAA's Coastal Services Center now has 2.4 meter land cover data for all eight of the main islands of Hawaii. Here is the link to the Coastal Change Analysis Program (CCAP) high-resolution land cover download page: http://www.csc.noaa.gov/digitalcoast/data/ccaphighres/download.html Select from the drop down menu ...


3

Kitex You could do a SQL Select that would calculate the areas. MapInfo Professional also has a built-in ProportionOverlap() function that could be used to calculate the proportion/percentage. Select BORDER.ID, CartesianArea(Overlap(BORDER.OBJ, COVERAGE.OBJ), "sq m") "Overlap" , ProportionOverlap(BORDER.OBJ, COVERAGE.OBJ) "PropOverlap" From BORDER, ...



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