With COG and dynamic tiling you create tiles at the time of request from the raw data. Usually this lets you apply rescaling or color correction to enable a better look for web map display. With mosaicJSON and rio-tiler-mosaic, introduced in COG Talk 2, we extended the idea of dynamic tiling by adding the pixel selection operation. When we have multiple overlapping datasets, you can tell rio-tiler-mosaic which pixel you want to keep or what operation you want to perform on the stack of pixel.
Ref: https://registry.opendata.aws/dataforgood-fb-hrsl/ Format: .TIFF (Cloud Optimized GeoTIFF)
The dataset is formed of 294 Cloud Optimized GeoTIFF, representing six different variables stored in separate file.
$ aws s3 ls dataforgood-fb-data/tif/month=2019-06/country=ZWE/ --recursive | grep ".tif$"type=children_under_five/ZWE_children_under_five.tif type=elderly_60_plus/ZWE_elderly_60_plus.tif
type=men/ZWE_men.tif type=women/ZWE_women.tif type=women_of_reproductive_age_15_49/ZWE_women_of_reproductive_age_15_49.tif
See it live: https://cogeo.xyz/projects/Facebook/index.html
Earlier this week in COG Talk 4 we shared how to do large scale processing using Cloud Optimized GeoTIFF and mosaicJSON. Here is another example of how to create simple visualization tools when you store the data as Cloud Optimized GeoTIFF.
Format: .LAZ (Point Cloud)
The dataset is formed of 684 different COG created from .LAZ file using a modified version of our cogeo-watchbot-light stack. Each COG has a 25cm pixel resolution and two bands (Min and Max, see PDAL docs).
See it live: https://cogeo.xyz/projects/MTLidar/index.html
COG Talk, which looks at ways to use Cloud Optimized GeoTIFFs to efficiently render and analyze planetary data at massive scale.
After a refresh of what COGs are in Part 1, the introduction of mosaics in Part 2, and a fun experiment in Part 3, today we are going to see how COGs can be useful for large scale spatio-temporal dataset.
First, the basics. As of today, the Cloud Optimized GeoTIFF specification can be summarized as a tiny list of requirements:
Basically, you take a well known open format (created in…
Cloud Optimized GeoTIFF is an excellent format for storing remote sensing data because the file structure provides a convenient method for data access and visualization. When we want to access a smaller raster — either as an array for analysis or a PNG/JPEG for visualization — we can easily read just that portion of the data. Most tools stop at this point and return a raster value, which is exactly what we want in most…
COG Talk, which looks at ways to use Cloud Optimized GeoTIFF, and why we use them.
The first post is a refresh on the COG format and announces the release of version 1.0.0 of rio-tiler and rio-cogeo. Here, we’ll see how we can use them to build mosaics for web maps.
Cloud Optimized GeoTIFF files, as the name implies, are specifically designed for easily accessing remote raster data. Because of the internal tiling and internal overviews, people often ask: can COGs replace map tiles? The usual response is: yes, but…
Cloud Optimized GeoTIFF can replace
.mbtiles or statically generated map tiles by using a proxy to render tiles dynamically (e.g lambda tiler). …
COG Talk,which looks at ways to use Cloud Optimized GeoTIFF, and why we use them.
For more than a year, we’ve been working on building out a suite of tools to make Cloud Optimized GeoTIFFs (COGs) easy to work with. Today we are excited to announce we are releasing version 1 of rio-tiler and rio-cogeo 🎂!
Both modules are:
Let’s start with a quick refresher on the COG specification:
COGs are powerful because of how the data is structured internally. If done properly, the data can be accessed via HTTP range requests, meaning you can read only a…
Note: This post was originally called: `The Ultimate data format`
In this post we will focus on Cloud Optimized GeoTIFF and other formats used by public dataset (AWS pds, Digitalglobe Opendata, …). This post is mostly a brain dump of some though and knowledge I needed to share since the remotepixel's huge AWS bill happened last august. I hope this will give some clue or at least some idea to people who want to open/share raster dataset.
First, can you guess the difference between both images 👇
Making COG at @DevelopmentSeed & Creator of @RemotePixel