GIS to DSM combination and essential change location..


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GIS to DSM combination and essential change identification. Timothée Bailloeul 11 Dec, 2003. Results over Beijing region - zoom Asian Diversions Stadium – Beijing (1999) Presentation
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GIS to DSM combination and fundamental change identification. Timothã©e Bailloeul 11 Dec, 2003.

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Results over Beijing territory - zoom Asian Games Stadium – Beijing (1999)

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Introduction Background : some Digital Surface Models (DSMs) have been produced from ethereal pictures (urban environment). DSM are coarse and have pixels with no height data. Issue : we need to combine the DSM data with the building layer of GIS information to : Add a third measurement to the GIS information : 2D  3D Check out if GIS-to-DSM change identification is conceivable Key focuses : How to consolidation GIS and DSM information ? Measure the change identification strategy

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Introduction Here results towards GIS-to-DSM combination utilizing geocoding data will be exhibited. Evaluating the DSMs geocoding precision is then vital. Geocoding data will likewise be utilized to blend the created DSMs. A straightforward technique going for recognizing changes between GIS information (old) and the DSM (new) was done.

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Contents DSM geocoding quality assessment DSMs blending Basic change recognition

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1. DSM geocoding quality assessment GIS TO DSM FUSION AND BASIC CHANGE DETECTION

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1. DSM geocoding quality assessment Around 10 Ground Control Points (GCPs ) are utilized to evaluate geocoding exactness of a DSM. For each GCP we know: Its directions in the Beijing System (East,North,Altitude) Its picture arranges (row,col) in the non-orthorectified DSM. For each GCP : We gather the (E,N,A) data from the non-ortho DSM at the GCPs (row,col) area, and figure the distinction.

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1. DSM geocoding quality assessment Results for DSM1

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1. DSM geocoding quality assessment Results for DSM2

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1. DSM geocoding quality assessment Results : geocoding exactness is extending from 0.5 to 1 meter sufficiently good for GIS-to-DSM enlistment (1-1.5 pixel) DSMs combining utilizing geocoding data can be tried

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1. DSM geocoding quality assessment GIS-to-DSM enlistment GIS and DSM information are both anticipated in the same cartographic framework. GIS building layer is a rundown of basic polygons (curved or sunken) Each polygon is a sub-rundown of vertexes which organizes are in the Beijing framework (cyclic rundown) Each polygon is anticipated on the DSM by (E,N,A)  (row,col) transformation.

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1. DSM geocoding quality assessment GIS-to-DSM enrollment - results

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1. DSM geocoding quality assessment GIS-to-DSM Registration - results

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1. DSM geocoding quality assessment GIS-to-DSM Registration - results

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2. DSM blending GIS TO DSM FUSION AND BASIC CHANGE DETECTION

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2. DSM blending Merging n DSMs is not all that simple : Within the covering region, what worth to pick among n ? Might we take the mean, median,…? Step by step instructions to oversee exceptions ? Arrangement : Take into record the between consistency of the DSMs, i.e. the scope of the DSMs height qualities Take into record the intra-consistency of the blended DSM, i.e. the intelligibility of each DSM esteem versus the neighborood of the officially combined pixels.

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2. DSM blending Few definitions for n=2 : Inter-consistency of 2 DSMs. DSM1 i,j and DSM2 k,l are predictable if Intra-consistency of the consolidated DSM and DSM1. DSM1 i,j is reliable with the intertwined DSM neighborhood if Where Neigh_DSM o,p is the mean registered over a 5*5 causal window in the effectively blended DSM.

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2. DSM combining Algorithm for n=2 and inside of the covering region : Build the geocoded jumping box For every pixel of that lattice : Check if both DSM have elevation data If none has height information, REJECT If one and only has, ASSIGN the worth If both have If the focuses are between steady  take the mean elevation of the focuses that are intra-reliable Else take the estimation of the most intra-predictable point

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2. DSM blending Result :

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Results over Beijing region - zoom Results : Olympic town region

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Results over Beijing territory - zoom Results : Olympic town region Merged DSM over covering range utilizing the exhibited technique Merged DSM over covering region utilizing the normal system just

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3. Essential change recognition GIS TO DSM FUSION AND BASIC CHANGE DETECTION

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3. Essential change recognition Problem explanation : GIS and DSM information can be effectively enrolled utilizing their geocoding data Transfer elevation to GIS layer DSM is coarse so exchanged height data is likewise coarse (middle) Basic change discovery (CD) is alluring Gives a from the earlier to the later GIS-to-satellite picture CD DSM is coarse so the strategy must be basic and will give restricted results

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3. Fundamental change identification What is a change ? In this present reality :

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3. Fundamental change location What is a change ? From guide irregularities : Mistakes from photograph translators who made the GIS Unaccuratly extricated structures layouts (revisioning)

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3. Fundamental change identification What is a non-change ? In this present reality : Isolated unaltered building with level rooftop Isolated unaltered building with non-level rooftop Not separated unaltered building

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3. Fundamental change recognition What would we be able to do with the DSM ? Outline a basic CD strategy : Using measurable worldwide criteria (mean, middle, change) towards elevation data from DSM. Powerful to the GIS and GIS-DSM coordinating errors. Restrictions : DSM is coarse, change location may be constrained to the most straightforward and clear CD cases. The executed strategy can deal with cases : a,c*,i

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3. Fundamental change location CD strategy For every polygon enrolled to the DSM, do : If rate of pixels with height information inside of the polygon OR in its neighborhood is < det_thres, then IMPOSSIBLE TO STATE Elseif the polygon and its neighborhood elevation is level (i.e. their standard deviation are < flat_thres) then : If the height distinction between the polygon and its neighborhood is > build_height_thres, then UNCHANGED Else CHANGED Else the building is delegated AMBIGUOUS

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3. Essential change identification Results with : det_thres = 10% flat_thres = 1m build_height_thres = 3m GIS information : 1996 DSM : 1999

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3. Fundamental change identification Flat_thres = 1m Build_height_thres = 3m

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3. Fundamental change location Det_thres = half Build_height_thres = 3m

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3. Essential change discovery Det_thres = half Flat_thres =1.5 m

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3. Fundamental change discovery Comments towards the outcomes : Few structures are characterized UNCHANGED or CHANGED (reliable to the beginning speculation). The more particular the parameters, the better the achievement rate. The evenness parameter quality is basic since the achievement rate is the most touchy to it. Couple of structures were accounted for as unaltered It would bode well to look at the quantity of structures recognized as CHANGED to the ones that have truly changed in the truth (requirement for ground truth).

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3. Essential change recognition Comments towards the outcomes : The outcomes rely on upon : The DSM quality (% pix with height data) The GIS quality The GIS-to-DSM coordinating precision The scene :extremely thick urban ranges won’t yield part of results

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3. Fundamental change identification How to enhance the outcomes : Normalize with a Digital Terrain Model (DTM) nDSM=DSM-DTM So we dispose of the territory height variety Possibility to utilize some supreme elevation limit, then no compelling reason to register the area elevation Make a few investigations with GIS of 2001 to accept the system. « Hysteresis » to get more results : Start from particular to freely parameters to have distinctive level of trust in the outcomes.

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CONCLUSION GIS TO DSM FUSION AND BASIC CHANGE DETECTION

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Conclusion DSMs geocoding precision evaluation has been exhibited DSM geocoding is exact and accept the camera parameters improvement process. GIS-to-DSM and DSMs combining procedures have been additionally appeared. An essential GIS-to-DSM change recognition was presented The quantity of prepared structures is restricted It is parameter subordinate (3). Specific parameters yield best results. Further change are conceivable Can give extra earlier data to GIS-to-satellite picture CD

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3. Essential change recognition

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3. Fundamental change identification

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3. Essential change recognition

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