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Dropbox/Google Drive work on the principle that when a file gets modified, it will upload the file into the cloud. Then when your on your other machine, it will download the latest copy.
This works great for small files.
GIS data can often be large filesizes.
If I edit one feature attribute in a layer that is 2GB big, there will be a lot of uploading/...
I have done several projects in this regard, but at the end they always ended up being custom solutions that basically
separated the problem in grids
did the processing in each individual node and copied the result to a temp table / data store
merged all the solutions to a single result table and optionally handled boundary conditions. Handling boundary ...
stored in file geodatabases
File geodatabases are the enemy of open source - if you change this to PostGIS or shapefiles, or similar, you'll have more luck.
Otherwise you're looking for a ton of features. You'll have some luck with GeoServer, but otherwise you might want to reconsider the scope of what you're looking for.
Use the GDAL/OGR VSI (virtual file system) syntax. For a URI, you would use /vsicurl, for a zip you would use /vsizip and you can chain them together, for a zip URI you would use /vsizip//vsicurl (note double slash).
So to add the poly layers from:
Update: Nowadays I prefer Plunker for this, primarily because it allows you to create any number of files. This lets you create more realistically sized/organized demo apps, rather than stuffing everything into one HTML/JS/CSS file. Here's a nice Leaflet example: http://plnkr.co/edit/Y9uk2G?p=info
I have found JSBin and JSFiddle to be extremely useful in ...
My suggestion would be to use the osgeo stack. Specifically, I have used this stack in the Amazon Cloud (AWS) to serve out large raster and vector data sets.
Postgresql with postgis stores my vector data
Geoserver / Geowebcache servers the vector data and tiles those large datasets for serving.
Everything runs through the browser using OpenLayers.
It can be done by using a pre-defined Geoserver Docker container.
Investing the time to learn Docker is really worthwhile as it allows you to stand up and replace your cloud Geoserver instances very quickly. Adding to the Dockerfile will also allow you configure Geoserver automatically with your workspaces, data sources and layers.
The best established Landsat cloud detection algorithm used today is fmask published by Zhu&Woodcock.
It is not written in R but in MATLAB, it can be downloaded as MATLAB code or as a compiled C executable. The source code however is openly available, so you could try to rewrite it in R (ceholden already did it for python).
Below answer is based on my experience in enterprise system design, which is heavily based on Esri solutions. This is just general advice based on what you have given.
shared across multiple companies working on the project who will also
be adding new data and updating old
Forget SHPs, forget fGDBs, store it in a DBMS that supports spatial ...
To answer both your questions.
It depends on your OS and the encryption software you use. I use Linux and have a LUKS encrypted external HDD. Whenever I plug the drive in, it decrypts the contents and I can access them normally. Note depending on what you use, you may have to decrypt your files to a local folder before you can access them with your GIS ...
This could easily be done with the tool r.series from the GRASS-repository. After you start r.series, select the rasters with the cloud coverage and select the aggregation method “count”. This should give you a result raster with the number of non NULL values from the time series.
It is possible. I recommend you to use Route 53, S3, CloudFront.
Host your domain name on AWS via AWS Route 53 DNS (yourdomain.com) to create a public zone.
Create a SSL Cert for your domain name via AWS ACM
Create a S3 Bucket (yourdomain.com) with the same domain name and enable website hosting.
Use the S3 Bucket Hosted Name to map into your recently ...
is the dopeness
geojson.io is a quick, simple tool for creating, viewing, and sharing maps. geojson.io is named after GeoJSON, an open source data format, and it supports GeoJSON in all ways - but also accepts KML, GPX, CSV, TopoJSON, and other formats.
has a basic free account
CartoDB is a cloud based mapping, analysis and ...
What you want can all be done with several different open source components. Nevertheless, your requirements are too ambitious, and you will not find a single package/installer that is a turn-key solution.
Host it at AWS. Look at Geoserver. Store it in PostGIS. Custom build with Django.
These things are Open Source, so it means you have different ...
Your feature collection is private, so I can't apply your code. But, here you have how to mask clouds from Landsat 5 Collection 1 Tier 2 TOA Reflectance and compute NDVI:
var region = /* color: #0b4a8b */ee.Geometry.Polygon(
If there are processing steps you need to perform on the data to only serve data selectively, you will have to do this serverside. You ...
We are currently supporting about 30 editors across the country on an m1.large machine in the Northern California region. It costs about $350/month. Our plan is to move to a m3.medium in the Oregon region. This should bring our costs down to less than $200/month. We run standard workgroup version of ArcGIS Server with SqlServer Express, SDE, GIS Services, ...
Google offers some nice tools. Check out google drive, fusion tables and shpescape.com (one of several sites that lets you import gis shapefiles directly to fusion tables)
There's a whole api built around this so you won't have to roll any of the client/server stuff yourself and I am only guessing, but i'd imagine being a google tool that it scales.
Amazon EC2 will be a good solution for your Geodatabases (though can get expensive being ESRI)
Scalable on demand—If you need more computing power, you can launch
additional EC2 instances, which you can think of as virtual servers on
Amazon's cloud that are all created from the same parent AMI. Creating
new instances can even be done ...
It is probably some quite large datasets you have to handle, and therefore i would perhaps not suggest a WPS solution, since you would be transferring data with the process request.
WPS 1.0 has limited capabilites for asyc. requests - which will be enhanced in version 2.0 - making the solution with WPS a little more feasible i guess - but for now and in ...
Take a look on pyWPS, an OGC Web Processing Service implementation. Its easy to install on a python environment. Most of the examples use GRASS GIS as pyWPS only implements the interface for remote handling, but it is possible to use any GIS backend to do the actual processing work.
So check out the gallery first, to get an idea what is possible and ...
You can get what you want by standing up a database service using Amazon RDS, and then installing a web-accessible database management on another system. This would permit you and your client to make queries against the database without requiring any software installation on your local computer.
If you are familiar with EC2 and basic system administration, ...
If you order your data through the USGS Earth Explorer via the USGS ESPA application you can use the issued cloud mask file with ending 'cfmask' or 'sr_cloud' (depending on which product you use).
These files simply contain bit keys for cloud presence. You cloud for example query these values with an if-statement and use the resulting dataframe as a mask in ...
You need to install tomcat on it, then use tomcat to install Geoserver Web Archive.
use https://bitnami.com/stack/tomcat/cloud/google to install tomcat,
then follow this video to install geoserver
Yes, I've read up on this being possible, at least through the OpenGEO Suite which is offered by Boundless Geo which packages Geoserver as part of their stack. I came across this release from Boundless Geo that mentions GeoMesa's collaboration and efforts to provide geospatial analysis and leverage Geoserver for spatial processing on Google Cloud.
You could ...
You can definitely host mapserver in azure. Here's a blog post detailing the installation process on a virtual machine. I do not get into configuring mapserver, just how to install it on a virtual machine.
Regarding the azure ...
Which version of QGIS are you using?
I used QGIS Free Plan to test but I noticed QGIS 3 does not support labeling whereas the older version of 2.18 support labeling exactly as set in the QGIS project. I suggest you try an order version.
The cloud masking you provided removes individual pixels that are considered to be clouds. You keep the whole image and just set cloud pixels to no data.
Filtering for clouds in this case is looking at the metadata properties associated with each image and removing whole images if it does not meet the criteria (or sorting based on the criteria).