alukach

# .github/workflows/app.yaml
name: My Python Project
on: push

jobs:
  test:
    runs-on: ubuntu-latest
    timeout-minutes: 10

    services:

alukach

import urllib

import boto3
from botocore.exceptions import ClientError

s3 = boto3.resource("s3")


TMP_FAILURE = "TemporaryFailure"
FAILURE = "PermanentFailure"

alukach

#! /usr/bin/env python3
"""
A utility to stream records from one or many S3 Inventory reports, with a progress bar.

./parse-inventory-progress s3://my-bucket/path/to/my/inventory/2019-12-15T00-00Z/manifest.json > out.csv
"""
import json
import csv
import gzip
import sys

alukach

from typing import IO
from dataclasses import dataclass, field
from io import BytesIO

import boto3
from PIL import Image

s3 = boto3.resource("s3")

alukach

{
  "bundles": {
    "analytic": {
      "name": "Analytic Radiance",
      "description": "Orthorectified product, calibrated to at-sensor radiance",
      "assets": {
        "Landsat8L1G": [
          "analytic_b1",
          "analytic_b2",
          "analytic_b3",

alukach

/*
In this file, we subclass the built-in Error object to create custom errors.
https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Error
*/
// tslint:disable:max-classes-per-file

abstract class CustomError extends Error {
  constructor(...params: any[]) {
    // Pass remaining arguments (including vendor specific ones) to parent constructor
    super(...params);

alukach

Organization

The basic idea of this organization is to link up software developers with organizations (from here on out referred to as "partners") that need custom software development.

Motivations

For Developers

Developers may be interested for the following reasons:

alukach

Generating building geometries from satellite imagery

Goals

Currently, the OpenStreetMap dataset for the Calgary, Alberta, Canada region is lacking most of the city's building footprints. The City of Calgary possesses a dataset of the building footprints, however it opts to sell this data rather than distribute it for free via its open data catalogue. The City of Edmonton does distribute its respective building footprint dataset freely and thus the footprints are available via OpenStreetMap. Utilizing high resolution satellite imagery of the City of Edmonton and the building footprint dataset from OpenStreetMap, it should be feasible to train a neural network classifier to generate building footprint polygons from provided satellite imagery of the Calgary region.

Methodology

A similar challenge Kaggle competition exists: Dstl's Satellite Imagery competition

alukach

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import json
import os
from BaseHTTPServer import HTTPServer
from SimpleHTTPServer import SimpleHTTPRequestHandler


if __name__ == '__main__':

alukach

#!/usr/bin/python -u

import json
import sys
from pygments import highlight
from pygments.lexers import JsonLexer
from pygments.formatters import Terminal256Formatter

for line in sys.stdin:
    if line.strip():