Category: Data

24 January 2019

Deeposlandia 0.5 has been released!

Are you interested in automatic image analysis, curious about deep learning applications, do you love geospatial data? You may appreciate the last release of our deep learning framework dedicated to image semantic segmentation! Here comes the 0.5 release of our R&D project! What’s new? In the last version of this open source project, some appealing
18 January 2019

Visualization of borehole logs with QGIS

At Oslandia, we have been working on a component based on the QGIS API for the visualization of well and borehole logs. This component is aiming at displaying data collected vertically along wells dug underground. It mainly focuses on data organized in series of contiguous samples, and is generic enough to be used for both
29 November 2018

A new collaboration between Oslandia and Inria

Oslandia has a team specialized in geospatial data processing and data science. We focus on geostatistics, Machine Learning, and other advanced algorithms. We follow carefully the state of the art research improvements in this scope. We are pleased to begin a new collaboration on the topic of urban sprawl with STEEP, a research laboratory part
12 November 2018

How to implement a geospatial data pipeline for deep learning

After focusing on street-scene images, our deep learning framework is currently exploited in order to analyze aerial images and building footprints with semantic segmentation algorithms. We recently extend these last works by investigating an instance-segmentation use case. Context In parallel to the last FOSS4G conference at Dar es Salaam (Tanzania), WeRobotics recently organized the Open
12 June 2018

Tempus 2.6 is out

We are still working at improving Tempus, our multimodal route planner. We recently published a new major version, that has been funded by Cerema, DRIEA and IFSTTAR, and we would like to share the recent changes that have been made to the project. Isochrone computation The core engine is now able to compute multimodal isochrone
7 May 2018

Deeposlandia 0.4 has been released!

On a previous article published on this blog, we introduced our work dedicated to convolutional neural network. We are now happy to announce the 0.4 release of this R&D project! What’s new? Until the last released version (0.3.2), neural networks were built with the TensorFlow library. The major modification comes with the transition to the
30 January 2018

Predict bike availability at bike sharing stations

In a previous article, we described the clustering of bike sharing stations in two french cities, i.e. Bordeaux and Lyon. We saw that geospatial clustering is interesting to understand city organization. Results were especially impressive for the Lyon data set. Here we propose to continue the effort about bike sharing system description by attempting to
27 December 2017

OSM data classification: code release

After a set of blog posts published the last summer, we are glad to announce that the dedicated code (version 1.0) has been released on Github. Our OpenStreetMap history data pipeline will let you analyze user contributions through time, following several modalities: evaluate the area evolution in terms of nodes, ways and relations; investigate on
29 November 2017

Cluster bike sharing stations around french cities

Oslandia team is involved in a constant effort in geospatial data gathering and analysis. By taking advantage of the recent trend in public open data releasing, and after reading an inspiring work done for Dublin data, we decided to evaluate the situation in our own living places. Here comes the open data portals for both
25 October 2017

Detecting objects starting from street-scene images

Exploiting artificial intelligence within the geospatial data context tends to be easier and easier thanks to emerging deep learning techniques. Neural networks take indeed various kinds of designs, and cope with a wide range of applications. At Oslandia we bet that these techniques will have an added-value in our daily activity, as data is of