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:CSname: STAC
:Title: STAC Community Standard Work Item Justification
:titletext: STAC Community Standard
:doctype: community-standard
:encoding: utf-8
:lang: en
:toc:
:toc-placement!:

jbants

:CSname: STAC API
:Title: STAC API Community Standard Work Item Justification
:titletext: STAC API Community Standard
:doctype: community-standard
:encoding: utf-8
:lang: en
:toc:
:toc-placement!:
:toclevels: 4
:numbered:

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STAC Sprint 4

June 4-6, 2019

Lots of work around API changes, support for ML/Data Science, and cleaning up core spec/extension examples and schemas. Groups were mostly focused on prepping for a 0.8 release but had 1.0 in mind.

API discussions:

• Subscritions and notifications for an AOI
• Not much movement on this.

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const path = require("path");
const url = require("url");

const Ajv = require("ajv");
const fetch = require("node-fetch");

const ITEM_SCHEMA_URL = "";

// load schemas from whatever source; keep track of the URI prefix that contains them to help with
// relative refs

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{
  "id": "landsat-8-l1",
  "title": "Landsat 8 L1",
  "description": "Landat 8 imagery radiometrically calibrated and orthorectified using gound points and Digital Elevation Model (DEM) data to correct relief displacement.",
  "keywords": [
    "landsat",
    "earth observation",
    "usgs"
  ],
  "version": "0.1.0",

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Overview

When you're working on multiple coding projects, you might want a couple different version of Python and/or modules installed. That way you can keep each project in its own sandbox instead of trying to juggle multiple projects (each with different dependencies) on your system's version of Python. This intermediate guide covers one way to handle multiple Python versions and Python environments on your own (i.e., without a package manager like conda). See the Using the workflow section to view the end result.

Use cases

1. Working on 2+ projects that each have their own dependencies; e.g., a Python 2.7 project and a Python 3.6 project, or developing a module that needs to work across multiple versions of Python. It's not reasonable to uninstall/reinstall modules every time you want to switch environments.
2. If you want to execute code on the cloud, you can set up a Python environment that mirrors the relevant

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Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

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# For Windows users# Note: <> denotes changes to be made

#Create a conda environment
conda create --name <environment-name> python=<version:2.7/3.5>

#To create a requirements.txt file:
conda list #Gives you list of packages used for the environment

conda list -e > requirements.txt #Save all the info about packages to your folder

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# ----------------------------------------------------------------------
# Remote Sensing Image Classification Workflow for Landsat data with soft
# voting on a SVM and Gradient Boosting classifier. Outlier in the 
# training data are flagged through an Isolation Forest algorithm. 
# Feature Selection is done by a Recursive Feature Elimination method.
# The results are classification and classification probability raster 
# images in TIF format.
#
# Written by Dimo Dimov, MapTailor, 2017
# ----------------------------------------------------------------------

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Get latest copy of gpsbabel from repo: https://github.com/gpsbabel/gpsbabel.git install qt5 build gpsbabel add to usr/local/bin

git clone https://github.com/gpsbabel/gpsbabel.git
sudo apt-get install qt5-default
cd gpsbabel
sudo make install