Some time ago, I released a plugin for QGIS called “Freehand Raster Georeferencer” that allows interactive raster georeferencing. It implements some tools (move, rotate, scale, stretch…) to manipulate a raster directly on the map, on top of the other layers. This is in contrast with the standard raster georeferencing tool of QGIS, which needs control points and then an export to be able to check the georeferencing.
Above, the Move tool.
Once the georeferencing is satisfactory, it is possible to export a new raster with a world file and then import it into QGIS in order to use all the available raster analysis tools. The main limitation of the plugin is the lack of support for all the GDAL raster formats: The plugin actually implements its own raster layer that uses Qt to perform reading and only some raster formats are supported (BMP, JPEG, PNG).
The code is opensource and on Github. Here is some documentation on the tools.
Some time ago, I saw this article about using the Qgis2threejs plugin to export a QGIS map as a 3D visualisation in the browser, thanks to three.js and WebGL. When I recently tried to follow the post to reproduce the results, I had some problem sourcing the data (related to Vienna, Austria) so instead I searched for data related to Paris to create a similar scene. Here is the result corresponding to the screenshot above and another one with aerial photos, both showing the Montmartre area of Paris.
The first step was to create a standard QGIS map with all the necessary data:
- Base map: OSM Mapnik tiles. To display them in QGIS, there are multiple ways: Either use the OpenLayers plugin, by selecting the OpenStreetMap layer, or the TileLayer plugin, by first copying this tile description file into the ~/.qgis2/python/plugins/TileLayerPlugin/layers/ directory and then selecting the OSM layer with the plugin.
- Elevation data (DEM): I downloaded the tile that covers most of France from the SRTM Tile Grabber. It provides a Tiff that will be displayed by QGIS in levels of gray by default.
- Tree data: a SHP layer with the locations of trees lining the streets of Paris (Arbres d’Alignement), obtained from the French OpenData portal (data.gouv.fr). I actually added the same layer twice in the QGIS project, so that in Qgis2threejs, I could export one of the layers as the trunks of the trees (cylinders) and one as the leaves (spheres). I kept one of the layers visible, rendered as a circle with some transparency so it would look like a shadow of the tree, and hid the other.
- Building data: a SHP layer with the location of buildings in Paris (Volumes bâtis), obtained from the Paris OpenData portal. It only contains the first 50000 buildings of the full dataset. The full data is pretty big, so this will do for my needs. If necessary, the GeoJSON dataset contains all the buildings.
The Qgis2threejs plugin will use what is currently in the QGIS map window as the texture for the 3D terrain. Therefore only the layers that should be in the output should be kept visible: This is why some of the layers are unchecked in the QGIS UI shown in the screenshot above.
All the layers, even if not visible in the QGIS map, can be configured to be output in vector form, potentially with some transformation applied, like an extrusion or a sphere. These are the settings that I used:
- DEM: The SRTM elevation data.
- Tree leaves: Green sphere of radius 3m at 5m above the ground.
- Tree trunks: Beige cylinder of radius 0.5m and height 5m at 0m above the ground.
- Buildings: Extrusion of 10m, with random colors.
I also applied some vertical exaggeration in the World settings.
After launching the conversion (which does not take very long for such a small extent), the browser opens a web page with the exported visualisation.
Above, an alternative version, with aerial photos.
I released a free app called Tokyo Ramen Map a few weeks ago on the Play Store. It is a simple map application showing all the ramen restaurants in central Tokyo, along with ratings. I built it for my needs so it is quite bare bones. The main point was to make it work offline since I don’t have a data connection outside my apartment.
It uses cartographic data from the OpenStreetMap project, rendered on the device through the Mapsforge library. The positions and ratings of the ramen restaurants come from scraping the RamenDB website. All the code for the app is open source and available on github. It can also be used to generate OpenStreetMap-based apps for any city, with the option of pre-loading points (like the Ramen app) or letting the user add their own. As an example of the latter, I have released apps for Tokyo (Tokyo Offline Map), without the ramen shop layer, and Geneva (Geneva Offline Map).
Recently, I have been researching techniques for indoor positioning, where GPS does not work. Although it is not widely deployed yet, one technology that looks quite interesting is called “iBeacon”, an Apple-designed (and currently unofficially documented) profile of Bluetooth Low Energy.
An iBeacon uses a Bluetooth signal to broadcast an ID to any listening device (most likely a mobile phone). From that, the distance from the iBeacon can be estimated from the signal strength and additional processing could yield more precise positioning. The iOS SDK also supports built-in geofencing and notification with iBeacons in the Core Location Framework. The use of Bluetooth Low Energy makes power consumption quite low and an iBeacon could last a long time even on a small battery so that only minimal maintenance is required.
As to reading an iBeacon signal from a phone, on the Apple side, it only works on iOS7 (and the hardware has to support it, although iPhone 4S and up do). On the Android side, version 4.3 has added the necessary Bluetooth LE support in the SDK and there already is an open source Android library to interact with an iBeacon. On the broadcasting side, an iPhone can serve as an iBeacon for testing purposes and there are prototypes of dedicated low-powered devices (for example, Estimote).
There are lots of potential applications for indoor positioning and not just on the marketing / retail sector, for example in the healthcare or home automation fields. Of course, iBeacon is not the only game in town for that (Wifi positioning comes to mind) but the fact that it is already being deployed bodes well for its chance of success:
Version 2.0 of QGIS (formerly known as Quantum GIS) has just been released! The codename for this version is Dufour.
QGIS is a user-friendly Open Source Geographic Information System (GIS) licensed under the GNU General Public License (GPL2). QGIS is an official project of the Open Source Geospatial Foundation (OSGeo). It runs on Linux, FreeBSD, Mac OSX, Windows and Android and supports numerous vector, raster and database formats and functionalities.
QGIS provides a continuously growing number of capabilities provided by core functions and plugins. You can visualize, manage, edit, analyze data and compose printable maps.
On top of the desktop software QGIS is well known for, the project also offers QGIS Server (to publish maps and data as OGC WMS and WFS) and a version for Android (coming soon).
Some additional links: