<p>This is what I have done:
First, using the parameters for LCC projection I did a coordinate transformation on the weather charts using QGIS. For this the charts needs to be georeferenced first based on lat-lon values of grid intersection. After transforming the charts in LCC to rectilinear grid I implemented &quot;detectMSERFeatures&quot;, a computer vision tool in MATLAB, to identify the desired pixel. The pixels have one-to-one relationship with lat-lon using which I was able to get the desired feature's coordinates.</p>

CC BY-SA 4.0
2023-12-07T13:28:03.057Z
2023-12-07T13:28:03.057Z
Sathish Vh
<p>I have collection of images of synoptic weather charts downloaded from Bureau of Meteorology (<a href="http://www.bom.gov.au/cgi-bin/charts/charts.view.pl?idcode=IDX0102&amp;file=IDX0102.202001010000.gif" rel="nofollow noreferrer">BoM</a>) which is in GIF format. I'm interested in obtaining the lat lon coordinates of cold fronts (thick black lines with triangles). My idea is: First get the pixel coordinates of the cold fronts. After obtaining the pixel coordinates, get the lat lon coordinates based on map projection type. <a href="http://www.bom.gov.au/climate/austmaps/about-rain-maps.shtml" rel="nofollow noreferrer">BoM</a> website mentions that the map projection type is Lambert Conformal Conic (LCC) projection. How can I speed up obtaining the pixel coordinates? And how to get the exact lat lon coordinates?</p>
<p>First, I approached extracting pixel coordinates in two ways:</p>
<p>Manual:- Obtain the pixel coordinates using the ginput function of MATLAB where with the help of mouse cursor I got the pixel coordinates.
Image processing:- Converted the image into Binary and used edge and boundary detection. But in some cases this is not working properly.</p>
<p>% CODE IMAGE PROCESSING %<br />
img = imread('IDX0102.201501010000.gif');<br />
binaryImage = imbinarize(img, 'adaptive');<br />
edgeImage = edge(binaryImage, 'Canny');<br />
BW2 = bwareaopen(img,3000);<br />
contourData = bwboundaries(edgeImage);</p>
<p>For obtaining lat lon coordinates based on LCC, I tried in MATLAB using pix2latlon function and also in python using the pyproj. None of them gives the expected results. How do I approach this problem?</p>

CC BY-SA 4.0
2023-11-01T10:04:58.963Z
Interesting stuff.  I'd think you have to work out if it's getting the pixel wrong, or converting that pixel to projection wrong.  One issue with various tools is that some flip lat/lon or x/y, so perhaps the wrong order.  But I'd suggest to narrow down if the pixel seems correct (can put the image into a program like Gimp and get the pixel location from the cursor in X, Y on screen easily to verify).  Then give more details on what types of answers it's giving or such.
JeopardyTempest
CC BY-SA 4.0
2023-11-01T10:05:26.463Z
I hope you'll give results if/when you finally get there, as it sounds neat :)
JeopardyTempest
CC BY-SA 4.0
2023-11-03T14:28:54.260Z
Also LCC is not a single projection but a family of projections parameterized by central meridian and a pair of standard parallels. While central meridian here is clearly 130E, the standard parallels would require some guessing to figure out.
Dima Chubarov
<coordinate-system><projection>
How do I obtain latitude and longitude coordinates from synoptic weather charts
Sathish Vh
# How do I obtain latitude and longitude coordinates from synoptic weather charts

(Asked by: username_0 on 2023-11-01)

I have collection of images of synoptic weather charts downloaded from Bureau of Meteorology ([BoM](http://www.bom.gov.au/cgi-bin/charts/charts.view.pl?idcode=IDX0102&file=IDX0102.202001010000.gif)) which is in GIF format. I'm interested in obtaining the lat lon coordinates of cold fronts (thick black lines with triangles). My idea is: First get the pixel coordinates of the cold fronts. After obtaining the pixel coordinates, get the lat lon coordinates based on map projection type. [BoM](http://www.bom.gov.au/climate/austmaps/about-rain-maps.shtml) website mentions that the map projection type is Lambert Conformal Conic (LCC) projection. How can I speed up obtaining the pixel coordinates? And how to get the exact lat lon coordinates?

First, I approached extracting pixel coordinates in two ways:

Manual:- Obtain the pixel coordinates using the ginput function of MATLAB where with the help of mouse cursor I got the pixel coordinates.
Image processing:- Converted the image into Binary and used edge and boundary detection. But in some cases this is not working properly.

% CODE IMAGE PROCESSING %  
img = imread('IDX0102.201501010000.gif');  
binaryImage = imbinarize(img, 'adaptive');  
edgeImage = edge(binaryImage, 'Canny');  
BW2 = bwareaopen(img,3000);  
contourData = bwboundaries(edgeImage);

For obtaining lat lon coordinates based on LCC, I tried in MATLAB using pix2latlon function and also in python using the pyproj. None of them gives the expected results. How do I approach this problem?

**Tags:** coordinate-system, projection

> **Comments:**
> **username_1**: Interesting stuff.  I'd think you have to work out if it's getting the pixel wrong, or converting that pixel to projection wrong.  One issue with various tools is that some flip lat/lon or x/y, so perhaps the wrong order.  But I'd suggest to narrow down if the pixel seems correct (can put the image into a program like Gimp and get the pixel location from the cursor in X, Y on screen easily to verify).  Then give more details on what types of answers it's giving or such.  
> **username_1**: I hope you'll give results if/when you finally get there, as it sounds neat :)  
> **username_2**: Also LCC is not a single projection but a family of projections parameterized by central meridian and a pair of standard parallels. While central meridian here is clearly 130E, the standard parallels would require some guessing to figure out.  

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## 1 answers

### Answer by username_0 on 2023-12-07. Score: 2 | Accepted answer

This is what I have done:
First, using the parameters for LCC projection I did a coordinate transformation on the weather charts using QGIS. For this the charts needs to be georeferenced first based on lat-lon values of grid intersection. After transforming the charts in LCC to rectilinear grid I implemented "detectMSERFeatures", a computer vision tool in MATLAB, to identify the desired pixel. The pixels have one-to-one relationship with lat-lon using which I was able to get the desired feature's coordinates.


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