fabiochiu/medium-articles huggingface dataset:
SpaceNet 7 Results: arXiv Paper and Winning Model Release
By: Adam Van Etten
Tags: Data Science, Open Source, Mapping, Machine Learning, Satellite Imagery

Preface: SpaceNet LLC is a nonprofit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e., building footprint & road network detection). SpaceNet is run in collaboration by co-founder and managing partner CosmiQ Works, co-founder and co-chair Maxar Technologies, and our partners including Amazon Web Services (AWS), Capella Space, Topcoder, IEEE GRSS, the National Geospatial-Intelligence Agency and Planet.

Building tracking and change detection from moderate satellite imagery is surprisingly tractable, as we’ve demonstrated in the last few DownLinQ blogs. The SpaceNet 7 Multi-Temporal Urban Development Challenge yielded some impressive results, with some interesting phenomena noted in analysis blogs 1, 2. This post serve as the final installment in the SpaceNet 7 analysis series, and we are pleased to announce that the winning models are now available in the open source. Furthermore, our paper on the SpaceNet 7 dataset and initial results can now be found on arXiv. In the sections below we explore performance with an interactive map, and provide details on how to access the models.

1. Winning Models

Recall from our results announcement blog that the top-5 submissions to SpaceNet improved markedly upon the SpaceNet 7 algorithmic baseline, and applied a variety of clever approaches to improve performance. Table 1 below details the specifics of each model, while Figure 1 illustrates the SpaceNet Change and Object Tracking (SCOT) score vs inference speed (for an in-depth analysis of SCOT scores, see our previous blog).

Table 1. SpaceNet 7 winners (originally presented here).

Figure 1. Performance vs inference speed for the winning algorithms, up and right is best (originally presented here).

2. Interactive Map

Figure 2 links to an interactive (pan, zoom, hover) map that allows interested users to explore various metrics (F1, Track Score, Change Score, and SCOT) for the winning submission of lxastro0.
Behind the maps
By: Koen Van Den Eeckhout
Tags: Mapbox, Data Science, Mapping, Maps, Data Visualization

Behind the maps

🌍 If you have been following me on Twitter or LinkedIn, you probably already know that I adore well-thought-out, beautiful maps. But until recently, I didn’t have a huge amount of experience in creating maps myself. Last November, cartographer Topi Tjukanov (@tjukanov on Twitter) organized the second installment of his #30DayMapChallenge, which seemed like the perfect opportunity to get some more hands-on experience.

The challenge has almost no rules, except the fact that each day has its own topic. This topic can be pretty generic, such as ‘islands’ (November 16), more specific, such as ‘COVID-19’ (November 25), or completely out of the box, such as ‘NULL’ (November 19).

All maps, no rules? Sounds like a challenge I could enjoy, and learn some new skills in the meantime. For example, I’ve always wanted to get better at Mapbox, a powerful tool to create custom online maps.

Somewhat unexpectedly, I did manage to produce a map every single day in November, so I completed the challenge and ended up with a nice, varied collection of maps. For some maps, people have asked me where I found the data, how I processed it, or which tools I used for the design. In this post, I’ll try to give you a glance behind the scenes, and the necessary pointers if you’re trying to recreate some of them.

Day 1: Abandoned shopping carts
Mapping the Israeli Venture Capital Fund Ecosystem — 2021
By: Michael Safdie
Tags: Venture Capital, Israel, Mapping, Startup Nation

Since we last published our first mapping in 2019, the Israeli venture capital market has continued to grow and scale, perhaps at a faster rate than ever before. Today, Israel is home to more than 110 active local venture capital fund managers, raising over $3.5 billion across 35 funds in 2020 alone. As a fund of funds manager, we constantly aim to build strong dealflow and be exposed to all of the funds which fall under our investment scope, which is perhaps the most critical component in our ability to select the best funds in the market and later partner with them for direct investments.

Along with the evolution of the market in the past few years and the emergence of new managers, we are happy to share our most recent mapping of the Israeli venture capital fund ecosystem. The mapping is mostly focused on funds which predominantly invest in Israeli-related companies, in line with our investment scope at Sweetwood Ventures.

A few notes about our mapping: our mapping is mostly focused on active financial investors and excludes Biotech and Life Science funds, Publicly Traded Funds, Funds investing in liquid assets, corporate venture arms, angel investors, incubators and accelerators. We split the mapping into two fund categories — “Local Funds” and “Foreign Funds”. Local Funds include all funds (or private investment vehicles) which predominately invest in Israel-related startups regardless of the funds’ location. Foreign Funds includes all funds (or private investment vehicles) which are foreign-based and have a significant exposure to Israel-related startups, but only includes the most active funds falling under such a scope. All funds mapped were actively investing in new portfolio companies during 2019/20. This list is not exhaustive, and there may be funds which are not included in the mapping. If you think your fund was miscategorized or you have information about funds we may have left out, please reach out to us.
Administrative Infrastructures
By: Jia Zhang
Tags: Mapbox, Mapping, Public Data, Open Data, Data Visualization

The second reason we built this map is to encourage the visual exploration of different types of administrative boundaries. The map shows how different types of physical jurisdictions overlap and interact with each other throughout the city.

Jurisdictions serving different needs carve up the city differently. Fire companies, school districts, and police precincts have to take into consideration physical proximity. The political boundaries of congress, state assembly, and senate by comparison replace physical constraints with demographic ones. Another set of boundaries representing community boards and city council resembles a hybrid of the first 2. Yet all 3 groups of jurisdictions, with differing purposes and agendas, inevitably share every resident and place in the city.

It is no surprise that carefully crafted political boundaries stand out as oddly shaped. Here in my home neighborhood in Queens, I am represented by Congressional District 14. Opening up the “Congressional District 14” layer to take a closer look, I can see that in addition to being shared between the Bronx and Queens, it also intersects with 14 State Assembly districts, and 9 State Senate seats as well as 6 Municipal Courts, 16 Community Boards, and 12 City Councils Districts.

How does political representation intersect with the services provided by the city’s fire, police, and education departments? My school district (district 25) sits entirely inside the borough of Queens, but is shared by 3 congressional districts, 2 municipal courts, 6 state assembly representatives, 7 community boards, 5 city councils. Functional areas such as fire companies and police precincts share similar division when overlaid with the political.

If we live in New York City, then we live in a place where these 10 layers of administrative boundaries align and diverge. This map shows every space as one that is separated by borders and shared by intersections at the same time. Please use the link below to find your jurisdictions and how they connect you to the rest of the city: https://centerforspatialresearch.github.io/admin_infrastructure/

About the data: Geographical data is just a small subset of public data. Within this subset, we chose just 10 representative layers out of the geographical boundaries available. For data on New York City, you can find the physical boundaries of a large assortment of administrative jurisdictions in the nyc.gov data portal here: https://www1.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page

About the map: The map is created by detecting the intersections between the 10 layers of geographical boundaries. The intersections are built in QGIS, and the map is built using a combination of D3.js and mapboxgl. The code is free to use under creative commons and can be found here: https://github.com/CenterForSpatialResearch/admin_infrastructure
Earn more with Hivemapper through our Referral Program!
By: Hivemapper Network
Tags: Hivemapper, Built With Hivemapper, Earn Money Online, Mapping, Referral Program

TL;DR

Referring someone to become a Hivemapper Dashcam Contributor can earn you up to $500!

Start referring today!

We believe that it’s more fun to have people you know contribute to Hivemapper’s decentralized, global map. Whether you have a friend, family member, coworker, or anyone that you know that’s already driving around on any given day, they too can earn rewards through our Honey program. Now with our Referral Program, you and your referral can earn even more!

‌💡 How does it work?

For every referral you make, you and your referral earn rewards*. For every milestone your referral makes, you and your referral earn more! (*See conditions below)

‌⚙️ What are the conditions?‌

First, you must have an account with us in order to participate in the Referral Program. Referrals must map within our current dashcam active markets Referrals must be on the Honey’s Tile Attribution program and contribute with the approved dashcams in order for both you and your referral to earn these rewards. Here’s a breakdown of our Referral Program:‌

🐝 Grow the Hive:

For every referral, you get $50 and your referral gets $50

Condition: Referral must drive at least 35km (unique) or collect 15050 total unique tiles in their first 30 days

🚀 Milestones

Every time your referral hits one of the following milestones, you and your referral earn more!

If your referral hits a milestone of 250km (unique) or collects 111,800 total unique tiles in their first 30 days , you and your referral will get $100

, you and your referral will get If your referral hits a milestone of 500km (unique) or collects 215,000 total unique tiles in their first 60 days , you and your referral will get $150

, you and your referral will get If your referral hits a milestone of 1,000km (unique) or collect 430,000 total unique tiles in their first 90 days, you and your referral will get $200

🧐 How do you track my referrals?

In order to ensure you get credit for your referral, please send us your referral’s username (they can find this on the top right hand of their dashboard when they log in). From there, if/when they hit the milestones, we can make sure we pay you and referral the rewards you’ve accomplished.

(they can find this on the top right hand of their dashboard when they log in). From there, if/when they hit the milestones, we can make sure we pay you and referral the rewards you’ve accomplished. Note: We understand that purchasing a dashcam to be a Network Contributor can be tough. We’ve set up a program where we can help with this. If you or your referral is interested, please send us an email at contributor-support@hivemapper.com.‌

We’re very excited to have you assist in growing our Network Contributors to help create a decentralized, global map!‌

Happy Mapping! 🗺
Distance measurement using laser sensor — Part 1
By: Leo Tisljaric
Tags: Robotics, Laser, Mapping, Distance, Sensors

Intro

Well, here is the sensor!

Laser distance measurement sensor Hokuyo UTM-30LX-EW (Image by: Author)

We can see basic parts on the front side:

Power LED — an indication of the power (on/off).

Operation status led — different behavior depending on status (ex. always on if operates without errors).

Light transmission and reception plane — the sensor is inside this plastic cover. It has two planes, transmission, and reception planes.

And the backside:

Ethernet cable — used for communication wit the computer.

Power cable — for providing power to the sensor. It has three wires: brown (DC 12V), blue (0V-GND), and green (OUT). The sensor requires around 1A for normal operations.

IP reset switch — used for resetting the IP configuration.

Here are some important characteristics related to distance measurement and sensor’s limitations:

Field of view: 270°

Angular resolution: 0.25°

Measurement steps: 1080 (270/0.25=1080)

Max. distance: 30 m

Before setting up the sensor, please consult the official user manual.

Setup

1. Power supply

The sensor is powered through a power cable which includes three wires: brown (DC 12V), blue (0V-GND), and green (OUT). The sensor can draw a maximal current of 1A. I have used an old PC power supply for sensor testing purposes.

2. Communication

In this article, we will show how to connect the sensor with an ethernet cable. The sensor has an initial IP setup of 192.168.0.10. To be able to communicate with the sensor, the PC must be in the same network. On Windows PC, you can do it by following this tutorial. For example, you can use the IP address 192.168.0.100 with subnet mask 255.255.255.0

Measurements

After powering the device and connecting it to the PC, we can start with measuring the distance!

For that, we will use Urg Viewer application. It is a simple application used for visualization of the measurements and exporting the results. There are two options for connecting with the sensor using Ethernet or Serial connection (button in the upper right corner).

After connecting to the sensor, distance measurements are shown as a table (left plane) and the blue dots (right plane). In the image below, you can see the result of the distance measurement in my apartment.

Example of the distance measurements (Image by: Author)

Conclusion

In this article, the usage and basic setup for measuring the distance using a laser sensor are shown. You can use this kind of sensors for many projects like alarm systems, parking sensors, or creating the map for mobile robots.

Stay tuned for the next article in this series, where you will learn how to integrate this sensor into your custom applications.

If you have any questions or suggestions, feel free to comment or contact me!

Linkedin: https://www.linkedin.com/in/leo-tisljaric-28a56b123/

Acknowledgement

The sensor is provided by the Laboratory for Intelligent Transport Systems at Faculty of Transport and traffic sciences, University of Zagreb, Croatia.

References
The secret to cutting last-mile delivery costs
By: Gaurav Bubna
Tags: Delivery, Routing, Mapping, Places, Maps

Introduction

Your Point of Interests (POIs) are more than just latitudes and longitudes.

For companies that rely heavily on maps and navigation — such as deliveries, ride hailing, ecommerce, and logistics, inaccurate or improperly configured maps can be a massive cash-drain.

Over 71% drivers spend anywhere from 4 to 10+ minutes trying to find the exact drop-off location. This aside, over 95% drivers have faced problems with inaccurate mapping — it’s estimated that the logistics industry could save $2.6 billion annually with better maps. With last-mile delivery accounting for 53% of the shipping cost, this number becomes even more significant.

Before we can fix it, we need to dive deeper into the challenge itself.

For doorstep navigation, your destinations are harder to reach than just a pinpoint on the map — this is because there’s a huge difference between knowing where a place is versus knowing how to reach it.

As an example, let’s take The Plaza in New York — your mapping solution will (or at least should) include the exact location of the reception area as a rooftop POI — and that’s simply not enough.

The route you take is determined by a variety of factors — such as the type of vehicle you’re in, the purpose of your visit and more.

For example, as a hotel guest, you’ll enter the building through the main entrance if you’re in a car.

However, the staff, catering, and housekeeping vendors will use a designated staff entrance instead.

This is where the plot thickens (pardon the pun) — depending on your business, the requirement, and the various entrances and exits involved, each location can have multiple relevant POIs to navigate through.

It’s also why simply plugging in the ever popular ‘you-know-which’ mapping API isn’t enough. Like most consumer solutions, it simply can’t cater to the nuances an enterprise needs when it comes to mapping POIs.

When you’re a growing business that’s serving customers at scale, the limitations of consumer-oriented maps severely limit the doorstep experience for your customers.
Un nuevo algoritmo de coincidencia de mapas en tiempo real en Lyft
By: Fernando Augusto López Plascencia
Tags: Ingeniería, Datos, Mapping, Ciencia De Datos, Geolocalización

¡Gracias especiales a todo el equipo de Locations por ayudarnos a poner este modelo en producción!

¡Como siempre, Lyft está contratando! Si te apasiona desarrollar modelos cuantitativos de última generación o construir la infraestructura que los alimenta, lee más sobre nuestros puestos en Ciencia e Ingeniería y acércate a nosotros.

Referencias

[1] Quddus M., Ochieng W. & Noland R., “Current map-matching algorithms for transport applications: State-of-the art and future research directions,” Transportation Research Part C: Emerging Technologies, 15(5), 312–328, 2007.

[2] Newson P. & Krumm J., “Hidden Markov map matching through noise and sparseness,” Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems GIS 09, 336, 2009

[3] Tutorial de DiDi en IJCAI-19: Artificial Intelligence in Transportation (láminas 28–40)

[4] Map Matching @ Uber

[5] E. A. Wan and R. Van Der Merwe, “The unscented Kalman filter for nonlinear estimation,” Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. №00EX373), Lake Louise, Alberta, Canada, 2000, pp. 153–158.
A Toolkit for Recovery
By: Defenders Of Wildlife
Tags: Science, Environment, Mapping, Technology, Hear From Our Experts

The red wolf has lost more historical territory (99.7%) than any other large carnivore in the world. After being considered extinct in the wild, the recovery program for the red wolf was successful in increasing the wild population to over 150 animals. But now, due to political pressure, illegal management and agency inaction, there are less than 25 red wolves left roaming the wild. They are an incredible species in desperate need of recovery, but they are not the only species facing such a struggle.

Curtis Carley/USFWS

The natural question is “so what can we do to help these species recover?” As things stand, that question can lead to some confusion.

Species recovery can take many forms and requires expertise from a variety of disciplines. But currently, there is no consistent way to coordinate these efforts. For example, we might find a red wolf’s range map (a map of where the species lives), basic information about the red wolf, and scientific papers about the red wolf, all published in different places. Then we have to compile it, which often means working with different data conventions, terminologies, and other complexities.

This should no longer be the case as we approach 2020. In fact, at the Defenders-hosted Partnership for Action on Conservation Technology (PACT) workshop earlier this year, we gathered national leaders in conservation technology and found widespread agreement on the need for much better coordination and consistency in sharing conservation information.

At the Center for Conservation Innovation at Defenders, we are developing a platform to help address this issue. The platform is called Conservationist.io and will be a set of tools gathered in one place, where people interested in conservation with different backgrounds and expertise can come to collaborate and research species.

The aim of this project is to make it easy for people from all walks of life to bring together the wealth of knowledge we have regarding conservation. We want to make it easier to share and reach consensus on range maps for species like the red wolf. (The first tool in conservationist.io is aimed at collaborative, transparent mapping.) We want to help make the development of recovery plans faster. We hope to host a space where conservationists from different fields all work in the same place, using common tools, while developing solutions tailored to the myriad challenges facing wildlife conservation.

Ultimately, we think conservationist.io and the idea of open collaboration will make recovery for species like the red wolf faster and more efficient and we cannot wait to release our Beta version of the platform later this year. Hope to see you there!
Is the U.S. Headed Toward an Eviction Crisis?
By: Joe Distefano
Tags: Housing, Covid 19, Mapping, Urban Planning, Economy

As the COVID-19 pandemic continues to batter the U.S. economy with an unprecedented surge in unemployment, many American families are facing a new threat to their livelihood: eviction. This looming reality adds new and urgent questions about the fate of our economy: How many American families will face eviction without additional financial assistance after the federal CARES Act Pandemic Unemployment Assistance (PUA) expires on July 31, and where will these evictions be geographically concentrated?

To help answer these critical questions, we assessed rental risk by analyzing unemployment claims, housing costs, and underlying social vulnerability at the national, state, and local levels, using the UrbanFootprint Recovery Insights Platform.

The results are staggering. Across the country, nearly 7 million households could face eviction without government financial assistance. These are heavily rent-burdened households that have likely experienced job loss as a result of the COVID-19 crisis. This level of displacement would be unparalleled in U.S. history and carries the potential to destabilize communities for years to come.

Closing this rent gap will cost between $16.2 and $31.8 billion over six months. This gap, while a pressing issue in every state, is distributed unevenly across the nation’s diverse housing markets and driven by a confluence of job loss and high relative rental housing costs.

Where is COVID-related rental risk concentrated?

Of the 120 million households in the United States, 43.1 million are renters. Even before the crisis, 20.1 million (47%) of these renter households were rent-burdened, and 10.1 million (23%) were severely burdened, defined as those spending 50% or more of their income on monthly rent. A severely rent-burdened household is unlikely to have the ability to save for an economic shock like prolonged job loss or a health problem.

Unemployment has disproportionately impacted rent-burdened households in the US, which are two times more likely than the average renter to experience job loss. Honing in on the 3.4 to 6.7 million rent-burdened households that have experienced job loss as a result of the COVID-19 crisis, we see risk spread across the country. In general, the states and counties with the biggest rent gap tend to have large populations, high housing costs and high income inequality.
Mapflow.ai — new Buildings model and Kepler.gl implementation
Tags: Mapping, Machine Learning, Keplergl, Mapflow, Earth Observation

Remember we released Mapflow — our new app to work with Geoalert Platform Mapflow.ai? Hopefully you’ve had a chance to try it. If you haven’t, let me convince you to try some new cool features we’ve just implemented into Mapflow:

1. A new “high-density housing” AI-mapping model is available. It improves buildings automatic mapping in areas with terraced and other similar densely standing buildings, such as in the Middle East, in Africa etc.

A fragmet of densely built-up area in Morocco

While this new model is still in experimental beta, the credit cost for its use is very low. So you can test it. In case you do “urban mapping” and see this model applicable to your area of interest — we’ll be happy to learn from your experiments and to share the results of ours.

2. We’ve improved Mapflow visualisation capabilities through the implementation of Kepler.gl which is open-sourced by Uber. It is a simple but powerful tool for data scientists to explore and analyse geospatial datasets.

Now you can view your processing results in kepler.geoalert.io

In particular, you can change visualisation parameters such as fill, opacity, etc. (the user guide for Kepler.gl can be found here)

Moreover, you can visualise buildings in 3D if you have enabled the “building_height” option for your processing.

3D building visualisation using Kepler.gl

Playing around with Kepler I tried to visualise an extract from “Urban Mapping” — our building footprints product with population counts. For example, you can use “Hexbin” or “Grid” map types to sum population in adjustable grid and visualise it — even in 3D.

Population data for Moscow city and Moscow region visualised in Kepler.gl

However, you should be aware that all the data is rendered on the client’s side which can be a challenge for your PC performance.

There are also other ways to preview and visualise data in Mapflow.ai before you download it for importing into any specialised GIS software — you can head over to our embedded map viewer or to geojson.io which is capable for small datasets convesrion to the other geospatial data types. But be aware that geojson.io may hang your browser for good if you have more than a few tens of megabytes of data.

Stay tuned for more updates! Tell us what you think if you have any questions / suggestions regarding Mapflow.ai or other Geoalert products.
USA According to Coronavirus
By: Yanko Tsvetkov
Tags: Political Humor, Mapping, Maps, USA, Political Satire

Making sense of American culture wars and their role in times of viral politicization

In the West, the saying goes, common sense emerges not from divine guidance, but from culture wars disguised as popular entertainment. This is why, while Xi Jinping is struggling to merge capitalism with Confucian traditions, Donald Trump is alternating between a commander-in-chief and an anti-establishment rebel.

Western culture wars affect every aspect of life. Immediately after Covid-19 booked a flight from Wuhan to New York in early 2020, the last apolitical human act—the venerable sneeze—joined an endless list of politicized mundane things. Soon enough, the tiny droplets of rapidly expelled saliva replaced sexual assault as the main focus of partisan propaganda.

Some of us fondly remembered the days of old, when we pondered why veganism got bundled with gay rights or why, despite the best efforts of Caitlyn Jenner to prove that gender hardware and cerebral software are not intrinsically linked, the phrase conservative transgender knocked off exact estimate from the top of the oxymoron charts. In hindsight, that world seemed much simpler.

Alas, my job is not to fuel your anxiety, for that is a task best performed by the media. I am in the business of humor, which thrives when culture wars reach their apex. That’s because wars are fueled by passion, and passion is always funny. Just think of your best orgasm and then try to picture the ridiculous faces you make while you climax—the rift between illusion and reality would be unsurmountable.

Same goes for political fights. Caught up in them, a sneeze is elevated to a statement, but to a disengaged onlooker, it’s just a secondary bodily function. Acknowledging such dualities in human experience is at the fundament of Western civilization. In the East people derive societal norms from harmony and order, imitating an idealized Universe. That’s why you won’t see many maps making fun China’s handling of the pandemic. In the West, we derive them from chaos. Hence, this.

So, stay calm! Things are exactly as they are supposed to be.
The Election Ring Map sneaks into Ken Field’s new “Thematic Mapping” textbook
By: Alan Mcconchie
Tags: Mapping, Elections, Cartography, Data Visualization, Electoral College

I’m very pleased to see this refined version of the map out in the world, and I’m curious to hear if this map type sparks any new insights with other people.

For me it’s especially resonant not just as a new look at the regional dynamics of the election, but also because of how it illustrates the acute flaws in the Electoral College itself. The Electoral College is vestigial and archaic in many ways, consisting of a nationwide collection of winner-take-all competitions that depresses turnout and demotivates voters in non-competitive states, resulting in millions of wasted votes that make no difference in the ultimate outcome of the election. A fairer system would use Proportional Representation or a National Popular Vote so that all votes would count equally… but I could talk about that for days and days and it’s a topic for another time.

Anyway, as you can tell, election maps are my jam, and I’m so pleased to be included in Ken’s massive book of so many other fascinating maps. His book will be released next week (August 31st) but you can pre-order the eBook on Amazon right now.
Bringing advanced Visual Re-localization pipeline using scene coordinate regression with deep learning in ARwayKit
Tags: Localization, Augmented Reality, Spatial Computing, Mapping, Computer Vision

Scene Coordinate Regression Localization method

It is a fundamental problem in computer vision and robotics, and it refers to estimating camera pose from an image. Recent state-of-the-art approaches use learning-based methods, such as Random Forests (RFs) and Convolutional Neural Networks (CNNs), to regress for each pixel in the image its corresponding position in the scene’s world coordinate frame, and solve the final pose via a RANSAC-based optimization scheme using the predicted correspondences.

The framework consists of a deep neural network and fully differentiable pose optimization. The neural network predicts scene coordinates, i.e. dense correspondences between the environment's input image and 3D scene space created at the time of Mapping. The pose optimization implements robust fitting of pose parameters using differentiable RANSAC (RAndom SAmple Consensus).

The system consists of two stages: Scene coordinate regression using a CNN (top) and differentiable pose estimation (bottom).

Mapping Process for the new method

Compared to the traditional SLAM-based method (Simultaneous Localization And Mapping), the on-device experience will be different as tracking across frames won’t be required in real-time. Hence mapping will be much easy for beginners. The underlying AR Service, such as ARKit or ARCore, will be running in the background and will be required for mapping.

During the mapping process, the device will collect visual data, Camera Poses, and other metadata that will be processed in the cloud consisting of state of the art GPU’s such as Nvidia A-100. Once the map data is successfully uploaded, it can take from 6–24 hours of Training time on each map, depending on the map size and complexity of the environment.

Dynamically updating the Maps over time

This new method will enable the dynamic updating of maps in the cloud, given the map is used in production localization. The retraining will happen once successful localization request counts reach the threshold. An updated DL model will be applied to all next localization requests, enabling height accuracy in continuously changing environments.
Mapping GIS Data on a Basemap by Contextily in Colab
Tags: Esri, Colab, Geopandas, Mapping, Contextily

Mapping GIS Data on a Basemap by Contextily in Colab ecyY Jun 29·5 min read

Nowadays many online apps are map-based applications, such as Uber, how to map GIS data on a basemap is an important knowledge in developing apps. Very often, data providers would only provide GIS data with coordinates or polygons’ coordinates, and a basemap is required to be layered (matched) to show a map. Depending on different purposes, various types of basemap are used. For example, Open Street Maps are more commonly used to show spatial relationships by roads and streets, Stamen Terrain Maps are better for showing contours and land use patterns. This article shows how to map GIS data on different basemaps by using Contextily in Colab.

We have already discussed how to map by geopandas in colab before (Yiu, 2021), but it is based on the maps provided by geopandas’ datasets and cannot be changed. Here we show how to download GIS data from a public website and then map them on a basemap.

The first step is always installing software, here we install geopandas and contextily.



# see # plot GIS data on different basemaps by geopandas and contextily# see https://colab.research.google.com/github/bwsi-hadr/03-Intro-to-GIS/blob/master/03_Intro_to_GIS.ipynb#scrollTo=aOtbuNGUTB7k !pip install geopandas

!pip install contextily

import geopandas as gpd

import contextily as ctx # for basemaps

2. Then we collect a shape file (.shp) of the locations of HOS courts in Hong Kong provided by ESRI. The website is called “Open Geo-Spatial Data in Hong Kong” which provides 14 categories of GIS data. Here I explore the category of “Housing” where includes many different datasets. HOS Courts in Hong Kong is just one of them as shown in Figure 1.

Here I save the shp file to my google.colab drive first, and define the filepath as the filename and use geopandas to read the file.
The tree huggers
By: Jill Shah
Tags: Mfadt, Mapping, Haiku

It was a beautiful day for us to be assigned this exercise. We had to go about mapping Union square in teams for whatever we could imagine; smell, emotions, feelings, lamp posts and the list goes on. I was teamed up with a graphic designer, Benny, and it couldn’t be as fun with anyone else.

Out of the range of these topics we had to chose from, we chose to map nature. But one walk around the park and we realized that nature was vast and different in so many aspects. It was not going to be possible to represent every nuance of nature on a single sheet of paper. So then we decided to go about it in this very special, very fun and a very eccentric way.

And Viola! We were roaming the park hugging every tree that came our way. Yes, we hugged the trees! This allowed us to measure the girth of the tree trunks and determine how old they could have been.

The reason we chose to map this particular aspect of trees was because it allowed us to interact with them in this very unique immersive we. I felt at one with the nature around and it also rekindled this spirit of being sensitive and sustainable in my approaches to the world. The measurement of the girth of trees was not precise by this method..and it wasn’t intended either. We didn’t want to pull out this numerical sheet that noted the actual diameter. What mattered is that we were able to gauge and realize the history of the tree. The age of tree. The trees were like grand fathers; who make us sit under their shade and tell us stories in the summer. So again i would note, it was a beautiful summer day to do this exercise.

The map we created tells the story of these trees, the way they matured and it tells the story of the experience we had while making this map. The visual language is very simple, very minimal. It eliminates everything including all the aspects of plants and trees apart from their girth.

This map provides an opportunity to find more such innovative and immersive ways to combine human behaviors and how we simplify the things we see. It encourages me to look for more such different perspectives through which we look at things and then represent them in such simple visuals that it could be felt and understood by all.
Favourite Cartography July 2019
By: Rob Collins
Tags: UX, Mapping, Maps, Design, Cartography

I took the time on my recent holiday to collect my thoughts on a few of the maps I’ve found impressive over this past month.

I’ve tried to keep a note of those that most people will find interesting and a few that were atypical from those normally seen on Twitter.

As always I’ll include links to the original creators where possible including the location where I saw each map.

The following article contains all my own views, not that of friends, family or my work. Feel free to dislike some of the maps I’ve pointed out. I’d love to hear if you’ve found any of the findings interesting.

The Many Faces of Auckland

Carl Churchill seen on Twitter

This is a lovely example of how to map population and ethnicity across a city. The contrasting blue and red populations, depicting White European and Mãori ethnicities respectively, are clearly visible in different boroughs of the city.

Carl drills into why this might be the case in little inserts which add to the value that can be ascertained from the map itself. I think this also shows the value of using the white space to your advantage around the key interest points.

To be critical, I would say for those that don’t know Auckland well it’s missing key landmarks to help orientate the viewer. I feel human geographies should be complemented with traditional features in combination with the terrain map that was used here.

3D adaptation of a nautical chart (New Zealand)

Found on Twitter map by Andrew Tyrrell

Surprisingly another map from New Zealand that I saw this month. I have honestly never seen a nautical map with this aesthetic that is normally associated with a traditional DEM whilst still retaining the level of detail needed.

Opting from using colour for the underwater features means that traditional characteristics of shipping maps can be included.

Perhaps the only surprising thing about the map is that the country itself isn’t in the centre of the map but as this is merely a recreation in 3D I shouldn’t knock it.

Morphologies of Tourism (Barcelona)

I wanted to include this set of maps on the list as it’s a brilliant use of cartography through a number of maps to provide focus whilst describing answers to questions about the city.
How Projection Mapping Is Transforming Operating Rooms
Tags: Virtual Reality, Projection Mapping, Mapping, Operating Room

Projection mapping is an innovative art technology that has been used to project images onto stages and buildings. This state-of-the-art technology was so innovative and advanced in the entertainment and architecture industries that this technology has now been revolutionizing the medical field.

Projection mapping is changing the way medical operations and education works. Read on to learn more about how it is propelling the industry forward and changing the medical field.

1. Solving Liver Issues with Projection Mapping

Resections are often performed on patients that have liver cancer to control bleeding and maintain healthy liver function. In the past, resections were far more complicated, as there are no marked indicators outside of the liver to identify where to cut. Although the surgeon is able to locate the tumour itself by the CT scans, having the inability to know where to cut can make the operation increasingly difficult. With the introduction of projection mapping to the medical field, resections have become far more efficient. Simulations are conducted prior to the actual surgery by analyzing 3D images of the liver. Using very high definition 3D visualizations, preoperative simulation can ensure a smooth, effective surgery.

Read More

Dynamic Projection Mapping
Create Your Own Santa Tracker With Gatsby and React Leaflet
Tags: Mapping, JavaScript, Front End Development, Tech, Programming

Finding Santa

Now that we’re in a good place, let’s get our hands dirty and find Santa. To do this, we’re going to use Google’s unofficial, undocumented API. This means it’s possible this API won’t be available the day after this gets published, but let’s be optimistic.

Additionally, at the time of writing, it’s still showing last year’s destinations, so what we’re really going to be visualizing here is Santa’s previous year’s route — though the hope is this would reset on the 24th, and we’ll all be merry!

Before we get Santa, let’s first add a line back to our mapEffect function:

async function mapEffect({ leafletElement } = {}) {

if ( !leafletElement ) return;

}

What this will do is prevent the rest of our code from running in the event our map isn’t ready yet. The mapEffect function itself, as you can see in the Map component, runs inside of an instance of useEffect passing an argument of a ref to the map, allowing us to run some code after our component renders.

So once we have that line, let’s now fetch Santa’s route inside of our mapEffect function:



if ( !leafletElement ) return;

let santa, santaJson, route, routeJson;

try {

santa = await fetch(‘

santaJson = await santa.json();

route = await fetch(santaJson.route);

routeJson = await route.json();

} catch(e) {

console.log(`Failed to find Santa!: ${e}`);

}

console.log(‘routeJson’, routeJson);

} async function mapEffect({ leafletElement } = {}) {if ( !leafletElement ) return;let santa, santaJson, route, routeJson;try {santa = await fetch(‘ https://santa-api.appspot.com/info?client=web&language=en&fingerprint=&routeOffset=0&streamOffset=0 ’);santaJson = await santa.json();route = await fetch(santaJson.route);routeJson = await route.json();} catch(e) {console.log(`Failed to find Santa!: ${e}`);console.log(‘routeJson’, routeJson); }

console.log(‘routeJson’, routeJson);

}

Let’s break this down:

We grab Santa’s route via the API endpoint

Once we have his route, we grab the response in a JSON format to make it easier to work with

This is all wrapped in a try/catch so we can safely handle any response errors

Finally, we just log out our response for now

Santa’s route object in the web console

Now we have Santa and his route, which means we can see all the destinations in his route. If you dig in the response a little bit, you can see some fun things like how many presents were delivered to each location and the weather at the time.
How do edit adjacent polygons accurately and efficiently
By: Sumit Kumar
Tags: Leaflet, Mapping, Maps, Geojson, Geospatial

Professional applications using geospatial data often require an entire city, country or other area to be split into many smaller GeoJSON polygons. All these polygons combined should cover the entire area of the map without overlapping parts of gaps Which can make editing them a cumbersome process. Not anymore.

When drawn accurately with tools like Leaflet-Geoman, adjacent polygons share coordinates at their corners. With Leaflet-Geoman Pros Pinning-Feature, you can pin those coordinates together. The result is, you can move entire borders between adjacent polygons in one go, without having to re-align any of the adjacent polygons by hand.

Just activate the pinning option and start editing – your adjacent polygons will stick together, continuously cover the entire area without gaps or overlaps.

With Pinning, workflows for administrators of geospatial data are getting simpler and save hours of time when editing geospatial data. Leaflet-Geoman Pro, a plugin for Leaflet to create and edit geospatial data directly on the map, offers many time-saving and easy-to-use tools for GeoJSON manipulation. Try it in out online GeoJSON-Editor, or check out our libraries for developers.
Building A Smart City Platform
Tags: Noise Pollution, AWS, Smart Cities, Urban Planning, Technology

Live Outside Urban Data (LOUD) Smart City Platform by productOps with VANTIQ

Urban centers across the globe are undergoing rapid change and growth. Cities are becoming smarter, not just in the technology used, but how city planners design the spaces where we live, work, and play. In order to properly plan a city, the rules that govern it, and the ability to enforce the laws, city managers need the right tools and data.

Not Just Smart But Healthy

Noise pollution is pollution like any other and affects the health of citizens. Noise over 60 dB can cause stress and elevated heart rates. Lower-income zones have historically higher levels of industrial noise, disproportionately affecting community health. With the increasing mixed uses in our cities, city planners and managers struggle to create laws and ordinances that are fair and enforceable. The idea was to prototype and test an urban noise monitoring system that allows city officials and citizens to respond to noise issues in real-time.

What We Did

We built the LOUD (Live Outside Urban Data) platform that empowers citizens and officials to act on noise in real-time and create a better city. Key elements of LOUD include:

Edge devices to perform noise analysis

VANTIQ platform processes sounds and manages workflows

Machine learning using AWS SageMaker

A mobile app to allow public to report noise violations

How We Did It

Measure — Collection of real-time data from devices and third parties and historic data from APIs assess the facts. We created audio sensors that collect data in real-time and send it to the LOUD platform for storage and machine learning classification.

Decide — Workflow applications and collaborations process data and make decisions. We used an intelligence platform, VANTIQ, to analyze the data and make decisions on it.

Act — Client applications, notifications, and collaborations allow systems and users to take action. The LOUD platform sends notifications to users that allows them to collaborate and take action using a mobile app.

Plan — Managers and third party users can plan to improve real world systems. With LOUD, reports are created based on the gathered data to allow users to plan and improve operations.

Benefits of the LOUD solution

Simply, the LOUD solution improves the quality of life of its citizens. It helps local companies reduce the number of noise violations, city management the ability to make data driven plans for the future, and consumers the power to make more informed choices based on a city’s noise profile. By bringing together the right mix of applications and partners, LOUD was created in quick time to provide the solutions city managers need to address noise pollution challenges.
Whitepaper: Sustainable Smart Cities and the Power of IoT
By: Swathi Yellajosyula
Tags: Data Management, Data Analytics, Smart Cities, Analytics, Data

The United Nations estimates that 68% of the world’s population will be living in urban areas by 2050. Under the growing weight of socio-economic issues stemming from this rapid urbanization, it cannot be emphasized enough that there is an increasing need globally for smart city solutions.

Multiple, disparate smart city projects are being implemented across the world today. However, as these projects expand to overlap within a smart city ecosystem, operational inefficiencies are bound to develop. At the same time, managing data efficiently can be challenging in the era of big data that is marked by an expanding network of IoT devices. Without the ability to interconnect, interpret and draw insights from streaming data coming from multiple devices, smart cities can risk turning a blind eye to a multitude of possibilities.

The Success of a smart city essentially boils down to the efficiency with which city authorities are able to balance technology, data, security and interoperability in providing citizen-centric services.

With the aim to dive deeper into the intricacies involved in architecting successful smart cities and explore the anatomy of a comprehensive smart city solution, we have recently published a whitepaper titled “Sustainable Smart Cities and the Power of the IoT”.

TIBCO’s Connected Intelligence Platform has helped several key players across the globe emerge as forerunners in the smart city race. Our expertise in helping materialize several smart projects in the past has aptly positioned us to author this scoping document that provides readers insights into topics such as:

Trends, Challenges & Opportunities for Smart City Projects Anatomy of a Comprehensive Smart City Solution Key Capabilities of TIBCO’s Smart City Solution Key Success Stories

You can access our whitepaper here.

Co-authored by Ashwin Datla- Senior Solutions Consultant, TIBCO