Towards the Next Generation Road Survey

Over the past few weeks, I’ve managed to escape the office and get back to the field. With an impending change, it’s been a very refreshing time to get back into the mix – especially out onto the roads of Zanzibar.


Alongside work with scaling out Ramani Huria and working with (awesome!) colleagues on the signing of an Memorandum of Understanding between Ardhi University, World Bank, and DfID to support the development of curriculum (with ITC Twente) and the sustainability of community mapping in Tanzania for the next five years. I’ve been working on a side project to look at how machine learning can be used to assess road quality.

To do this, the N/LAB team at University of Nottingham and Spatial Info (the spin out of my team that helped build Ramani Huria/Tanzania Open Data Initiative) and I are working with the Zanzibari Department of Roads, under the Ministry of Infrastructure, Communications and Transport to survey all roads in Ugunja Island Zanzibar.

The Department of Roads & Uni Nottingham Team

So far, we’ve worked on getting a surveying vehicle back on the road, initial back and forth with government stakeholders, and working on pulling together the various road data sources (such those from the Government and OpenStreetMap) to work out where to drive and the sequencing of the survey. All of this will support a data collection protocol that merges traditional surveying techniques, with novel ones such as RoadLab Pro.

All of these data streams will then be used as a training dataset to see how machine learning can inform on road quality. But first, we’re getting the traditional survey underway. It’s going to be long road ahead – as long as all the roads in Zanzibar!

Watch this space, the project’s Medium page, and the N/LAB’s blog on using machine learning for automated feature detection from imagery. Get in contact below in the comments as well.

Written in the Al-Minaar Hotel, Stone Town, Zanzibar (-6.16349,39.18690)

OSM: Going Back in Time

I’ve been playing around with the full planet file to look at going back in time in OSM. Mainly, this is to look at how Ramani Huria’s data has evolved over time and is all part of extracting more value from Ramani Huria’s data.

I’ve been playing around with the full planet file to look at going back in time in OSM. Mainly, this is to look at how Ramani Huria’s data has evolved over time and is all part of extracting more value from Ramani Huria’s data. This process wasn’t as straightforward as I had hoped, but eventually got there – also, this isn’t to say that this is the only or best way. It’s the one that worked for me!

To do this, you’ll need a pretty hefty machine – I’ve used a Lenovo x230 Intel i5 quad core 2.6ghz, 16gb of ram with over 500gb of free space – This is to deal with the large size of the files that you’ll be downloading. This is all running on Ubuntu 16.04.

Firstly, download the OSM Full History file. I used the uGet download manager to deal with the 10 hour download of a 60gb+ file over 10meg UK broadband connection. Leaving it overnight, I had a full file downloaded and ready for use. Now to set up the machine environment.

The stack is a combination of OSMIUM and OSMconvert. On paper, the OSMIUM tool should be the only tool needed. However, for reasons that I’ll come to, it didn’t work, so I found a workaround.

OSMconvert is easily installed:

sudo apt-get install osmctools

This installs OSMconvert other useful OSM manipulation tools. Installing OSMIUM is slightly more complicated and needs to be done through compiling by source.

Firstly, install LibOSMIUM – I found not installing the header files meant that compilation of OSMIUM proper would fail. Then use the OSMIUM docs to install OSMIUM. While there is a package included in Ubuntu for OSMIUM, it’s of a previous version which doesn’t allow the splitting of data by a timeframe. Now things should be set up and ready for pulling data out.

Dar es Salaam being the city of interest, has the bounding box (38.9813,-7.2,39.65,-6.45) – you’d replace these with the South West, North West point coordinates of your place of interest, and use OSMconvert, in the form:

$ osmcovert history_filename bounding_box o=output_filename

osmconvert history-170206.osm.pbf -b=38.9813,-7.2,39.65,-6.45 -o=clipped_dar_history-170206.pbf

This clips the full history file to that bounding box. It will take a bit of time. Now we can use OSMIUM to pull out the data from a date of our choice in the form:

$ osmium time-filter clipped_history_filename timestamp -o output_filename

osmium time-filter clipped_dar_history-170206.pbf 2011-09-06T00:00:00Z -o clipped_dar_history-170206-06092011.pbf 

This gives a nicely formatted .pbf file that can be used in QGIS (drag and drop), POSTGIS or anything else. As the contrast below illuminates!

Tandale, Dar es Salaam, Tanzania – 1st August 2011
Tandale, Dar es Salaam, Tanzania – 13th February 2017

Enjoy travelling back in time!

All map data © OpenStreetMap contributors.

FOSS4G 2018: Dar es Salaam

This will be a different FOSS4G. As the first in a developing country, our vision is for an accessible and inclusive FOSS4G, a FOSS4G for All.

In 2013 I was fortunate to be on the Local Organizing Committee (LOC) of OSGEO’s Free and Open Source For Geospatial Annual Conference – aka FOSS4G. Each year, the location of FOSS4G is decided on the basis of Europe, North America and “Rest of the World”.

As Dar es Salaam, Tanzania is in the “Rest of the World”, when the call for proposals was released, myself and a small group of co-conspirators figured that we’d submit a letter of intent for Dar to be the host city. Ultimately, Dar es Salaam was selected by the OSGeo Board to be host city for 2018, co-chaired by myself and Msilikale Msilanga.

This will be a different FOSS4G. As the first in a developing country, our vision is for an accessible and inclusive FOSS4G, a FOSS4G for All. 

In aligning FOSS4G 2018 within the Sustainable Development Agenda, we aim to complement the existing OSGeo stable while empowering the Open Data, Participatory Mapping and Internet of Things work currently underway within Tanzania, the region and worldwide. This will expand the scope of the conference from traditional geography and location tech, to applications, use and best practices of our tools.

Location and geography are at the heart of the Sustainable Development Goals: FOSS4G 2018 in Dar es Salaam would invigorate our existing projects, bringing them to new users and developers while supporting and nurturing the existing OSGeo community.

The clock has started for the LOC to organise the conference and the press release from OSGeo is out! It’s going to be a few months till FOSS4G 2017 in Boston till Msilikale, our LOC and I will pick up the keys to the FOSS4G car. In the meantime the Dar LOC will assemble and start laying the groundwork for 2018. It’s going to be a long and rewarding road ahead!

Building Heights in Dar es Salaam

I first went to Dar es Salaam in 2011, there were a few skyscrapers adorning the city’s skyline, now they’re everywhere! Sitting on a rooftop bar in the center of the city, it’s a mass of cranes and pristine new buildings.

Alongside this rapid growth, Ramani Huria has been collecting a lot of data but a lot of it doesn’t get rendered by the default OSM styles… so I’ve dug into the data and created a map of the different floors across the city.

This interactive map allows you to explore where the tallest buildings are in the city, but in displaying the data in this way, also allows for the densest, unplanned and informal areas of the city to become very clear.

There is still some way to go though – in Dar es Salaam there are around 750,000 buildings, with roughly 220,000 (~30%) having been surveyed by the Ramani Huria team and given an appropriate attribute. Ramani Huria has focused its efforts in the urban centres of Dar es Salaam, where most of the multi-story buildings are to be found. But, still a lot more to be covered towards Bagomoyo and Morogoro.

Hat tip to Harry Wood who’s advice and guidance pointed me in the right direction – a more technical blog post and more details of other challenges around correctness of tagging but that’s for another post – now to look at Floor Spaces Indices…!

GDAL Nodata and Drone Processing Pipeline

Due to a nodata tag issue within the GeoTiff standard, Pix4D doesn’t generate the nodata value in a GDAL compliant manner. As such, when importing GeoTiffs into software using GDAL (like QGIS or Mapbox) the nodata value is black, meaning overlapping layer issues. Additionally, for an unknown reason, Mapbox does not deal with multiple GeoTiffs in the same manner as it displays .mbtiles, pixellating uploaded GeoTiffs. This short blog presents a processing pipeline to deal with both of these issues.

The solution for translating the nodata values has been documented here: I’ve written a simple bash script to convert this in a UNIX environment is here: Basically, run the script in the folder where files are found and it will iterate through each file, translating the nodata tag value to be GDAL compliant.

Once you’ve got the nodata/transparency values resolved, it’s quite easy to add your Geotiffs to Mapbox’s Tilemill. Export your resulting map as .mbtiles and upload this to Mapbox as a tiles. In the Mapbox Studio Classic interface, add this to a map and voilà, a drone imagery online.

Data Driven Governance

At the Africa Open Data Conference I was fortunate to have a chat with Juliana Letara, the Town Planner for Kinondoni Municipality and Osiligi Lossai, the Ward Executive Officer of Tandale. We discussed the recent community mapping, how they are beginning to use the maps and the data and some unexpected outcomes.

A longer blog will be incoming on how data is being used to support governance in Dar es Salaam. Personally, I’m still digesting some of the implications and potential of what they’re discussing, regarding the potential impact that Ramani Huria (also here could have and is having in Dar es Salaam.

Starting Ramani Huria – Mapping The Flood Prone Areas In Dar es Salaam

Four years ago, in August 2011 I was fortunate to manage the community mapping of Tandale. It was an experience that irrevocably changed my professional direction and interests. Over a month I trained and worked alongside brilliant students and community members, who were all focused on getting an open map of Tandale, something that had never been accomplished previously. When it was done, the reception across civil society and government was positive and intentions on scaling the pilot to the city were mooted but for one reason or another it never quite made it. Then in December, floods hit the city. In dense informal urban environments such as Tandale these floods are fatal and dramatically change the landscape as well as causing mass damage to survivor’s livelihoods and assets. Mitigating these floods are hard – where do you start in the fastest growing city in Africa? The population as of the 2012 census currently stands of 5 million, with projections showing it could grow to 10 million by 2030.

This rapid and unplanned urbanisation is in part the cause of flooding: the infrastructure with which to cope with high rainfall, such as drains and culverts, were not built alongside residential dwellings. This is especially acute in the unplanned, informal urban settlements where a majority of Dar es Salaam’s residents reside. The theory here is quite simple: If that if you can identify where it floods, you can either install or upgrade infrastructure to ameliorate the situation for residents. Unpacking this, the crux of the issue falls to two main points, governance and data.

Ramani Huria – Swahili for “Open Mapping” – is a operationalization of this theory of change. In March 2015, a coalition from across Tanzanian society, composed of the City Council of Dar es Salaam, the Tanzanian Commission for Science and Technology (COSTECH – under the Ministry of Science, Communication and Technology), the University of Dar es Salaam, Ardhi University, Buni Innovation Hub supported by the Red Cross and World Bank supported the inception of Ramani Huria, with the goal of mapping flood prone areas in Dar es Salaam, making this data openly available and supporting the use of this data into government where decisions can be made to mitigate flooding.

Mapping Phases
Mapping Phases

It is a far cry from 2011 where just mapping the ward of Tandale was a large task. Ramani Huria consists of a pilot phase and four subsequent phases. To pilot, the wards Ndugumbi, Tandale and Mchikichini, with a combined population of over 100,000 residents were mapped in series. This process combined 15 students matched with community members, leading to maps of all features within that community. This information, focusing on drainage and water ways, is critically needed to help understand and locate flood prone areas; this is high priority in Dar es Salaam due to the damage that annual floods wreak upon the city and its residents. In this piloting phase, conducted from March to the end of June these three wards were mapped, in part to generate the data that will generate flood inundation models and exposure layers but also to pilot the data model and gel the team, prior to Phase One.

Scale Up Workshop
Scale Up Workshop –

Phase one on paper is quite simple. Take 150 students from the University of Dar es Salaam’s Department of Geography and Ardhi University’s School of Urban and Regional Planning on industrial training, hold an inception workshop, deploy this contingent across six wards and work with community members to replicate the pilots, but running in parallel. At the time of writing, mapping is ongoing in six communities: Msasani, Keko, Makumbusho, Mabibo, Makurumla and Mburahati. According to the 2012 NBS census, these wards have a combined population of over 280,000 residents. Phase one was kicked off on the 6th of July and will run until the 14th of August.

Field Survey -
Field Survey –

Phases Two and Three, will integrate community volunteers from the Red Cross, these volunteers are committed to creating community level resilience plans. These plans will use the data produced by the mapping to create resident evacuation routes and aid Ward Exective Officers with planning decisions among many other uses. Additionally, with embedded long term volunteers monitoring change in their wards, this will hopefully result in detailed up-to-date maps in rapidly changing urban areas.

InaSAFE Training -
InaSAFE Training –

Phase Four unfortunately sees the students depart from the project, due to their graduation. With a remaining contingent of around 30 mappers, mapping will continue until February 2016. These phases cover the data component, consequently alongside these phases are dedicated training events aimed at building capacity to use and deploy this data in real world situations. On the 20th July the first such workshop series took place, with representatives from the Prime Minister’s Office for Disaster Management Department being trained in spatial analysis in QGIS and risk modelling using the QGIS plugin InaSAFE. A series of these workshops will take place, placing the data into the hands of those responsible for the city.

While this is ongoing in Dar es Saalam, you could get involved wherever you are in the world, through the Missing Maps project. Missing Maps is a collaboration between the Red Cross, Doctors Without Borders and Humanitarian OpenStreetMap Team, aimed at digitising “the most vulnerable places in the developing world”, but primarily do so by crowdsourcing the digitisation of aerial imagery. At the moment, there are three tasks for Dar es Salaam:

By helping digitise the buildings and roads, using the recent drone and aerial imagery, the process of mapping is faster, allowing the community mappers to focus on the detail of flood data. Additionally, the data from Ramani Huria is all placed into OpenStreetMap, its code is on Github and content available from Flickr and Facebook, all with an open licence. Please get involved!


Written on a plane somewhere between Tanzania and the United Kingdom