Grassland v0.1 (alpha version)

Grassland is an expanding, peer-to-peer network of incentivised and anonymous computer vision software that turns video feeds from fixed-perspective cameras around the world into a (politically) stateless, indelible public record of the lives of people and the movement of vehicles as a simulated, real-time, 3D, bird's-eye-view similar to the games SimCity® or Civilization®[1], but with the ability to rewind time and view events in the past.

A form of inverse surveillance[2] with a unique, neural network proof-of-work to provide distributed, accessible and verifiable information about the physical world

For low power machines like the Raspberry Pi, this (lite) node version is used in tandem with a Serverless AWS Lambda function performing object detections. It allows "infinite" horizontal scaling of AI object detection inference at maximum FPS without having to buy expensive hardware.

No SLAM, Point Clouds, RGB-D, Lidar or Other Sensors Needed!

Works With Any Video Feed

Download links are below under "Alpha Version"

Use Cases:

A citizen or public servant who wants to know what any citizen or (rival) public servant is doing at this moment or has ever done before
Data science. I now know most of my neighourhood's average height, individual walking gait identification pattern, estimated salary based on car model, family structure, daily schedules, how many (visibly) pregnant women, on what days in July the guy across the street mowed his lawn, the pattern he mowed it in the 8th time and that it was the same day my other neighbour had 5 guests over for a get together. I can rewind and replay it from multiple angles in 3D. If I really want, I can convert it to spreadsheet format, etc.
Impartial verification of current or past events, an embargo on fake news, unaccountability and ignorance in any Grassland node's corner of the world at least
An autonomous source of income (See section, "Economically Self-Interested Nodes")
Fixed, low-latency, eyes-in-the-sky for a self-driving car to tell them precisely where the lane is but more importantly how human driven vehicles really expect each other to drive in this location and that there's a kid approaching on a bike on the other side of that truck that it knows the car can't see yet.
A city planner wanting to know exactly what problems need to be solved
A person whose decisions are based on property, traffic, demographics, etc.
e.g. a dentist wanting to know exactly, instantly and for free how many families live in this area
e.g. a person about to buy that gas station across the street and needs to know precisely how many cars go there a day and how much they bought estimated from how long they spent pumping and the size of the tank from the make and model
An insurance company, a marketing firm, sales rep etc. And not merely statistical information. Grassland gets very, very specific.
An impartial, indifferent, semi-omniscient, non-human oracle for a smart contract
You're buying a house. You obviously want to know everything about the people that live in the neighbourhood
You're selling a house. You can show the buyer nearly everything about the people that live in the neighbourhood
Plain curiosity about human behaviour, seeing people's lives, in an unbiased manner, from a bird's eye view and being able to play with time
For security
Useful at every network size even if there was only one node
The first use case will never be entirely precise for times when that person is in a place where there is an expectation of privacy. But you can make a lot of educated guesses with the right statistical models. Some of the more detailed, data science information will be added to the network's official model over the course of 2019 when the detail and computational requirements increase as per the schedule below divided into "eons".

Basic Properties:

The first 3 were taken as "mathematically axiomatic". i.e. Having assumed such a system is "true" in an abstract sense, I proceeded by logical means to deduce a programmatic theorem that follows from and satisfies those first 3 postulates.
1. Trustless: A system exists where you don't have to blindly trust a developer or any other node. You just have to trust mathematics. Successfully submitting fake data in the network is so computationally intractable that any self-interested node would find it more profitable to be honest
2. Economic Incentive: A system exists such that as long as there is someone out there who likes money, it would happen, in our case that the remaining "dark" areas of the map will be "lightened up"
3. Data Symmetry: A system exists wherein no party could maintain a data gathering asymmetry (one-sided surveillance) so long as there remain other parties acting in their own self interest. Advantage then must come from real innovation not parasitical organizations engorged on people's information. A "scorched earth policy", hence the name "Grassland"
No Central Authority: It follows then that even if all nodes were shut down and the only node still going was being run by some little girl in some place I'd never heard of, it would keep rewarding that faithful remnant with a war chest of money and a perpetually increasing and unparalleled knowledge of human behaviour and the environment. They'd end up with a very wealthy and frighteningly "omniscient", young person on their hands.
Anonymous: It follows then that all nodes are anonymous
Open Source: It follows then that the code is free to be viewed by anyone

Software Features:


Proof-of-Work Diagram

The results of each object detection on a frame is published along with cryptographic hashes from different stages in the neural network.
Since it's astronomically improbable to have hashed those hidden activations into the correct digest unless those hidden layers were actually computed, this is a very useful proof-of-work.



You can download the latest Grassland eon's object detection model using the following URL's. The node software can be downloaded here on Github

Eon Framework Download URL Notes
0 (current) Tensorflow 1.7.0 frozen_inference_graph.pb of faster_rcnn_inception_v2_coco_2018_01_28
1 T.B.D. ( Semantic Segmentation
2 T.B.D. ( License Plate Recognition
3 T.B.D. ( Walking Gait Recognition

Contact: david[at]grassland[dot]network

[1]. I am not affiliated with the owners of these software titles. They're owned by EA Games, Firaxis and Microsoft, respectively.

[2]. Reverse Surveillance and Undersight

[3]. Nash Equilibrium