Need to remove an embedded image from a PDF file? You can easily chop out parts of it as needed with the PDFtk command line tool and a little bit of text editing. Here’s how… Continue reading “PDF Map Hack – Remove Embedded Image”
Just a quick video follow up based on a reader asking how I did what I did in my previous post with Unity (http://unity3d.com) game engine.
Using Surface Wave asset and built-in Unity Terrain generator, plus a script for taking DEMs and creating Terrains easily. I’m really new at this by the way, but have a brilliant teacher showing me this stuff in my spare time
I’m helping some friends who are working on a project to visualise a whole whack of GIS data in Unity (Unity3D.com) game engine. It looks like we’ll end up working on a GIS -> Unity workflow for generating terrains from DEMs and texture maps from orthophotos. To top it off they’ve already got a landcover classification app running that takes landcover raster classes and creates 3D objects (grass, trees, water) in the model. (Don’t worry, I won’t tease you by mentioning their voxel based subsurface soil model interaction). It’s still early but really encouraging so far.
Next up is to simulate water flow in the environment and it was slim pickings for options for doing this. Then they found the Unity asset called Surface Waves (US$80) – it does the water flow work we wanted but much more. I just posted a really short test video to see how it worked – with both an auto generated water source and a manual placement water source, like a paint brush, that allows you to see how things will flow. It is amazingly performant on a notebook.
Be sure to check out Surface Waves’ demo video – it frees you from trying to emulate the look of water movement through shader trickery to actually simulating water flow over and around objects. Things that used to take a sophisticated GIS quite a while to compute actually, the last time I tried anyway
More to come as we play around with it, but I put it out there in case other spatially oriented folks might be interested as well in the GIS -> Unity workflow challenges being worked through. If so, I’ll do more video highlighting the work that’s underway.
Would love to hear how you partition your spatial data..
As data volumes grow, so does your need to understand how to partition your data. Until you understand this distributed storage concept, you will be unable to choose the best approach for the job. This post gives an introductory explanation of partitioning and you will see why it is integral to the Hadoop Distributed File System (HDFS) increasingly used in modern big data architectures….
Read the full article on Make Data Useful >>
I dug up one of my oldest blog posts from about 7 years ago. In the post, I show how I had connected my Garmin eTrex GPS receiver to an Arduino board and used it to control a camera in a desktop application.
After pumping the data into the Arduino, I parsed the raw GPS data coming from the eTrex and streamed it out to a Python app on the desktop, via a serial port. I was using the output to position a virtual camera in the OSSIMPlanet platform by formatting XML in Python and sending it to a listener built into the OSSIMPlanet app. (OSSIMPlanet is a sort of pre-Google Earth, Google Earth on steroids product.)
I haven’t used it for years, but thought some of the methods from this old post may still apply if you have an Arduino + GPS or Arduino -> Python streaming requirement. Enjoy the flashback – I know it’s inspired me to pick up a new Arduino to continue where I left off.
Recently I started an Internet of Things series on my experiences installing, using and analysing data from a smart electrical meter. This included a BC Hydro smart meter, Eagle monitoring gateway from rainforest automation, and a cloud-based analytics service from Bidgely.
I’ve collated all the posts on the topic for you below. More will be added as I write them. Enjoy!
After a week of collecting smart meter readings, I’m now ready to show results in a cloud-based energy monitor system – Bidgely – complete with graphs showing readings, cost and machine learning results breaking down my usage by appliance.
This is part 4 of a series of posts about the Internet of Things applied to Home Energy Monitoring. I have a Smart Meter from BC Hydro, an Eagle energy monitor and various cloud apps helping me understand it all.
3 Value Added Parts to Bidgely
In this post I’ll show you the three parts of Bidgely that I’ve found most helpful:
- Usage dashboard
- Cost dashboard
- Appliance breakdown (best for last!)
Working in the big data and analytics space, I’m always interested in parts of the Internet of Things (IoT) that will produce more data, require more backend systems, and help users/customers get on with their day better.
The past week has shown a few interesting announcements relating to Internet of Things topics. Here are just a few that jumped out to me, either because they inspired me or because I was left wondering what it would really mean.
TL;DR? Summary: While IBM is “getting started” (oops, I meant “getting serious”) and Facebook has big plans to “take over”, Amazon comes out with a consumer focused solution.
I like this concept: “Just press and never run out”. It’s the Amazon Dash Button: http://amazon.com/… – intended to be stuck onto appliances, basically retrofitting ones that don’t have them built in (in the future). Pressing a button orders refills of products, just like Amazon one click ordering online.
What would you put a button on? Scary or useful?
The Eagle energy monitor from Rainforest Automation is a very handy device. It reads the wireless signal from my electricity meter and makes it available through a web interface – both a graphical environment and a RESTful API. In this post we look at the standard graphical screens and the data download option. Next time we’ll look at the RESTful API for programmers to use.
This is part 3 of a series of posts about the Internet of Things applied to Home Energy Monitoring.