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Posts Tagged ‘3d’

Comparación de DTM usando Global Mapper 17.0.1

2016/02/12

Hagamos hoy algo sencillo, comparar, primero cualitativamente (visualmente) y después cuantitativamente dos DTM. Por un lado elegimos una fuente muy usual, SRTM de 3 arc sec (aproximadamente 90m) con un DTM derivado de Fotogrametría Stereo.

  • Comparación CUALITATIVA (i.e visual)
  • Comparación CUANTITATIVA (i.e RMSE)

Abrimos por un lado un DTM cuya fuente sea SRTM, en este caso me he conectado via WMS (Web Mapping Service) a través del data online disponible dentro de la misma aplicación Global Mapper (File/Download Online Imagery/data). La resolución es de aproximadamente 90m (3 arc sec).

DTM-COMPARISON-20160212

Por otro lado he encontrado este DTM cuya fuente conozco (Stereo Photogrammetry). La resolución es de 5m.

DTM-COMPARISON-20160212-02

A través de la herramienta ‘digitizer tool’ (Tools/Digitizer) seleccionamos una línea dibujada al azar sobre los dos. Botón derecho del ratón-> analysis/measurement/path profile. Exporto ambas imágenes (es importante en path setup definir un mismo mínimo y máximo para poder compararlas adecuadamente).

Con Photoshop superpongo (Layer display/ multiply) ambas imágenes y veo cuán diferente son.

DTM-COMPARISON-20160212-03

Esto nos da una primera idea de la comparación, pero vayamos un poco más allá: ¿Cuál es el RMSE (Error medio cuadrático, Root Mean Square Error) entre ambas bases de datos?.

DTM-COMPARISON-20160212-04

Esta es una medida de desviación que nos va a definir mucho más exactamente que una simple visualización. Podéis ver algo más desarrollado este punto en este link de esta misma página:

https://geovisualization.net/2010/06/30/using-excel-to-calculate-the-rmse-for-lidar-vertical-ground-control-points/

DTM-COMPARISON-20160212-05

Ahora tan solo hemos de verificar que esta cifra sea la correcta teniendo en cuenta los valores de precisión prometidos en la entrega.

Espero que os haya resultado interesante, si así es, no olvidéis comentar, compartir o simplemente decir Hola. Cualquiera de estas opciones es apreciada.

Un saludo cordial,
Alberto CONCEJAL
MSc GIS and Remote Sensing

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Change detection – Detección de cambios en polígonos

2015/10/22

change-detection-bogota-telemediciones-20151023-02
THE IDEA: DEMONSTRATING HOW DYNAMIC A CITY IS, THUS HOW IMPORTANT IS HAVING AN UPDATED DATASET
bogota-change-detection-20151105-02

THE FACTS: THE CITY OF BOGOTÁ IN COLOMBIA 2012-2014

Overall growth rate: -0.12% ONLY HAVING INTO ACCOUNT THE DIFFERENCE OF BUILDINGS CAPTURED BETWEEN 2012 AND 2014 (We can do this because we have used the same data capture model in both years)

(De acuerdo al censo catastral, para 2015 la ciudad incorporó 51.531 predios nuevos urbanos. En total, hay 2’402.581 predios en la ciudad, de esos, 266,9 millones de metros cuadrados son de área totalmente edificada. Source: http://www.eltiempo.com/bogota/crecimiento-bogota-/15394797)

bogota-change-detection-20151105

THE PROCEDURE: Centroids of buildings; Spatial join showing presence-absence, considering a 10m accuracy threshold, meaning if the centroid has not moved more than 10m, its the same building. If the centroid in 2012 is not in 2014, its considered as demolished. If a new centroid appears its considered new building.

DENSITY MAPS+3D buildings
Help to quickly focus on the highlights
bogota-change-detection-news-20151021

 

DTM validation using Google Earth (and RMSE extraction)

2015/03/10

Hi guys,

Surfing the internet is great when you need to figure out something. I needed to validate some DTM from unknown sources against an also unknown source (but at least a kind of reliable one, Google Earth).

All we need is

  • Google Earth
  • TCX converter
  • ARcGIS
  • Excel

This is the procedure i have followed:

  1. First of all we draw a path over our AOI using Google Earth, we save this as KML,
  2. This KML is opened by TCX converter, added heights and exported as CSV,
  3. CSV is imported by ArcGIS,
  4. We use the tool ‘extract multi values to points‘ to get in the same table the values of our DTM and the values from Google Earth,
  5. We use Excel to calculate the RMSE and get a quantitative result,

These are the values in our DTM

dtm-validation-02

This is the path we have to draw in Google Earth

dtm-validation-03

Using TCX converter we get the heights out of Google Earth’s DTM

dtm-validation-01

Using the tool ‘extract multi values to points‘ we get the heights out of our DTM

dtm-validation-04

We measure the differences and extract the RMSE.
Are we within our acceptance threshold or expected level of accuracy?.

You guys have to figure this out for yourselves!!!

Lost regarding RMSE calculation?. Think you have to take a look at this other post.

dtm-validation-05

dtm-validation-06

Hope you guys have enjoyed this post, if so, don’t forget sharing it.

Alberto Concejal
MSc GIS and QCQA expert (well this is my post and i say what i want :-))

Remote Sensing, Photogrammetry, Lidar and Landuse IGN Spain

2014/11/18

teledeteccion-fotogrametria-lidar-usos-del-suelo-ign-20141118b

A few more lines for leting you know again that i passed this other course just now in Instituto Geográfico of Spain (IGN).

Remote Sensing, Photogrammetry, Lidar and Landuse, a comprehensive 40h update on relevant information i need tu use on a daily basis. This ‘update’ helps me to better understand what i am working with and this way, being able to properly describe it for my daily analysis,

Advanced Thematic Cartography IGN Spain

2014/11/14

cartografia-tematica-avanzada-20141118b

A few lines for leting you know i passed this course last year 2013 in Instituto Geográfico of Spain (IGN). Spatial analysis, Spatial stats, proper simbolization, data mining and geovisualization. A very interesting 40h online course that helps me on a daily basis to be able to show geodata in a more professional way.

Because we normally need to deepen our geodata without making too complex to understand the result of our analysis.

HTML High resolution DTM visualization using Quantum GIS (Qgis)

2014/11/03

This QGIS Plugin, Qgis2threejs, exports terrain data, map canvas image and vector data to your web browser!!

3dvisualizatio-DTM-QGIS-20141103

All you have to do is opening the DTM in Qgis (2.4.0 Chugiak), go to plugins library and install Qgis2threejs.

3dvisualizatio-DTM-QGIS-20141103-03

Once its installed you will see this icon on screen iconand you will need to clic on it.

3dvisualizatio-DTM-QGIS-20141103-04

Then choosing the parameters of the visualization and voilá!!

I have used a 5m DTM which source was LIDAR so the quality is very good

3dvisualizatio-DTM-QGIS-20141103-05

Hope you guys like it. Feedback would be greatly appreciated.

Alberto Concejal
MSc GIS and Quality Control
albertoconcejal [at] gmail.com

DTM from SRTM? Let’s compare sources using RMSE (Root Mean Square Error) and a gaussian kernell density map

2014/10/29

I guess we all can make a DTM out of many sources but SRTM is one of the most common ones, right?. Then let’s learn from this very simple approach how close we are from the SRTM raw data.

  1. Selecting a not very big representative area to be able to handle it,
  2. exporting raster to polygon (from SRTM 3 arcsec/90m) dataset 1
  3. exporting raster to polygon 30m (our DTM dataset) dataset 2
  4. exporting to POIs 30m (our DTM dataset) dataset 2b
  5. Spatial join POIs dataset 2b vs dataset 1
  6. RMSE
  7. visualizing delta using a density map/gaussian kernell +appropriate symbolization

In yellow we see theres a full correspondence between SRTM and our DTM dataset and in blue there’s a ‘hole’ and in red there’s a ‘mountain’, this means it’s in here where the shift is more important.

This way we can highlight if sources are OK.

It’s simple but it works. How do you like it?. Please feel free to send some feedbak.
(Software used: ArcGIS 10.1, Global Mapper 13.2)

Cheers,
Alberto Concejal
MSc GIS, QC

DENSITY-MAP-V1-VS-SRTM-20141021

density maps parameters

rmse-sierra-leone-20141008-02

Spatial join between both DTM datasets

blog-20141029

Density map for highlighting differences between both datasets (ours and SRTM’s)

blog-20141029-03

RMSE. It’s not too big so there’s need to visualize to find potential bizarre spots

blog-20141029-02

bizarre DTM heights

RSME comparing LIDAR data with a third party’s 3D dataset

2014/05/02

I would like to share with you an easy analysis i have been working in the last days. I had a vector dataset of buildings and i knew how high they were (there was a field called ‘AGL’ or Above Ground Level) and a LIDAR 2m resolution dataset over the city of London. My aim was comparing both sources, understanding LIDAR data was the actual reality (or a closer version to it) and my source of 3D buildings was the dataset i needed to deliver to my customer…  Te actual height of those 3D buildings had been extracted using stereo photogrammetry methods. I also needed to focus on residential data, so heights below 15m… So make it easy. The question was:

How accurate is my dataset of residential buildings over London?. Which is the RMSE measuring them both?

I used Global Mapper v.13.2 (b062012) and ArcGIS 10.0 (b3200)

This is the 2m resolution LIDAR data provided by geomatics-group.co.uk

LIDAR-01

I also needed to get a layer of points out of this dataset so i used Global Mapper and went to Files/Export elevation grid format and choose ASCII as the format.LIDAR-06

This is the layer of buildings and their AGL as label
LIDAR-02

I flagged those residential buildings
LIDAR-03

and using ArcGIS i performed a Spatial Analysis using Arctoolbox/Spatial analysis to join the Lidar heights in ASCII format and the residential heights… to be able to measure the difference between both datasets

this way i got a new vector layer which table contained both elevation fields (Lidar and my 3D buildings)
LIDAR-07

As you can see, i added a new field in ArcGis using table/add field and added ‘compare’ and SQL [“AGL”- “ELEVATION”]
LIDAR-04

then i measured it visually using a density grid in Global Mapper. Create density Grid.
LIDAR-05

And finally measured the RMSE by opening the table in excell format and usign the actual formula for extracting RSME values:

= SQRT(SUMSQ(M1:Mn)/COUNTA(M1:Mn)) —> Note this formula is only valid for this case. You’d need to update Mx values using yours:-)

LIDAR-07

Wow! a very high value. Does this value corresponds to our accuracy figures? Yes? No?.

Now it’s the time for decission makers to bring into action!

LIDAR-08

And what about some geostatistical analysis. I performed this using North East Trends in ArcGis. We can see from West to East there’s no variation  but we can see it increases the error the further the south…

LIDAR-09

So this is the area concentrating the higher differences comparing both datasets.

Hope you liked the analysis, if so…share!!!!

LIDAR-10

Alberto CONCEJAL
MSc GIS

 

Projets éoliens en Loire-Atlantique

2013/09/02

C’est vraiment magnifique l’ouverture des données publiques… Je vais décrire mon itinéraire pour mieux comprendre:

  1. Télécharger des données (Projets éoliens en Loire-Atlantique)
  2. Telecharger DTM (SRTM v4)
  3. Faire Carte d’ombrage (ArcGIS),
  4. Orientations (ArcGIS) et
  5. Inclinations (ArcGIS) pour mieux comprendre l’emplacement des moulins
  6. Faire Carte de densité (en mesurent la puissance du parc)

eolicpark_03

Et maintenant la carte de densité en Global Mapper…

eolicpark_02

Name=Chauvé

Feature Type=Unknown Point Feature
Geometry=Point location: 321341.949 6688237.734 (Lat/Lon: 47° 11′ 11.0775″ N, 2° 00′ 13.7962″ W)
Map Name=projets_eoliens.shp
NOM_PARC=Chauvé
ETAT_AVANC=Permis de construire accepté
NBR_EOL=6
PUISSANCE=12

Et après, avec tous les cartes et toutes les données, faire l’interprétation, voici toutes les moulins du vent en Loire Atlantique, on peut apprécier la concentration au Nord/Nord-Est de la région:

eolicpark_01

Et aussi la rose des vents (http://www.nantes-erdre.fr/statistiques-du-vent-a-nantes):

La rose des vents représentative du secteur d’étude est celle fournie par la station de Nantes-Bouguenais.

Les données ont été recueillies sur une période de 29 années (entre le 1er janvier 1971 et le 31 décembre 2000).

La rose des vents ci-après représente la distribution annuelle des vents (tous mois et toutes heures
confondues).

Les vents sont classés selon trois catégories :
– vents dont la vitesse est comprise entre 5 et 16 km/h (bleu),
– vents dont la vitesse est comprise entre 16 et 29 km/h (vert),
– vents dont la vitesse est supérieure à 29 km/h (orange).

Ces catégories sont ensuite reportées en terme de fréquence pour chacune des 18 directions de la rose des vents située au centre (nord, sud, est, ouest, etc.)

Les vents dominants sont les suivants :

– Les vents de secteur ouest/sud-ouest et sud (directions de 180 à 280°) qui représentent 36,5% des vents, toutes vitesses confondues. Les vents les plus forts de la station (vitesse supérieure à 29 km/h) soufflent majoritairement dans ces secteurs.

– Les vents de secteur nord-est (24,7% des vents) avec une majorité de vents faibles ou moyens (directions de 20 à 80°).

frequence-vent
statistiques-vent0

image

Idéalement il faut exporté raster->vector et faire l’analyses spatiale mais ça va être un outre post !

Ici, quelques liens et information général:

L’energie eolique. http://www.loire-atlantique.fr/jcms/cg1_244375/l-eolien

Documentation: http://data.paysdelaloire.fr/donnees/detail/localisation-des-projets-eoliens/?tx_icsoddatastore_pi1[page]=4&visualization=3

Éolien terrestre ou off-shore, ce mode de production électrique devrait connaître une accélération sans précédent en Loire-Atlantique d’ici à 2020.

L’éolien apparaît comme la principale source d’énergie renouvelable électrique permettant d’atteindre dans les toutes prochaines années un niveau important de production.

C’est pourquoi Le Département a fixé en juin 2010 un objectif ambitieux de puissance éolienne installée à l’horizon 2020 :

  • 600 mégawatts (MW) terrestres
  • 500 MW en mer.

Solar + Shadows analysis on Rue Massillon, Nantes (France)

2013/04/04

This is the beautiful building in from of my house in Nantes… Let’s model it first using Sketchup. Also i have used a non standard style only for visualization purposes…

rue_massillon_02

If we have the North behind us this means we are going to have a lot of light… but so far we are just guessing…

rue_massillon_01

Now we know if for sure. Take a look at the results by M. Capeluto’s Solar Analysis.

rue_massillon_03

If we face South we notice there’s a lot of sun in the other side of the building (every line is a summarized path during a month, being the higher the closer to the summer solstice)

Please take a look at the video prepared by Tomasz Janiak, the developer of the tool.

Hope you guys find it interesting. Please let me know your thoughts.

Alberto