Land Use and Cover information is essential to support governments in decision making on the impact of human activities on the environment, for planning the use of natural resources, conservation of biodiversity, and monitoring climate change.

Currently, several projects systematically provide information on the dynamics of land use and cover. Well known projects include PRODES, DETER and TerraClass. These projects are developed by INPE and they produce information on land use and coverage used by the Brazilian Government to make public policy decisions. Besides these projects there are other initiatives from universities and space agencies devoted to the creation of national and global maps.

Although these projects adhere to open data policies and provide a rich collection of data, there are still a gap however in tools that facilitate the integrated use of these collections: it requires from researchers, students and public officials a great effort to collect, organize and integrate all the datasets, prior to their use. In general, each data collection adopts its own land use and cover classification system, with class names and meanings very different across the collections. Besides that, the collections have different spatial and temporal resolutions, rely on different data representation (raster or vector), and are served by different systems in several formats (files, DBMS or web services).

In this context, the Web Land Trajectory Service (WLTS) is a service that aims to facilitate the access to these various land use and cover data collections through a tailored API. The result is tool that allows researchers and specialists to spend their time in the analytical process, once the API provides the integration of these datasets and brings the concept of Land Use and Cover Trajectories as a high level abstraction. The WLTS approach is to use a data model that defines a minimum set of temporal and spatial information to represent different sources and types of data. WLTS can be used in a range of application, such as in validation of land cover data sets, in the selection of trainning samples to support Machine Learning algorithms used in the generation of new classification maps.

For more information on WLTS, see:

• WLTS Specification: the WLTS specification using OpenAPI 3.0 notation.
• WLTS.py: Python Client Library for Web Land Cover Trajectory Service.
• LCCS-WS Specification: which is used to represent the classes associated with the resources retrieved in WLTS queries.
• LCCS.py: Python Client Library for Land Cover Classification System Web Service.
• LCCS Server: Python Web Server for LCCS.

See INSTALL.rst.

See DEPLOY.rst.

Land Use and Cover information is essential to support governments in decision making on the impact of human activities on the environment, for planning the use of natural resources, conservation of biodiversity, and monitoring climate change.

Currently, several projects systematically provide information on the dynamics of land use and cover. Well known projects include PRODES, DETER and TerraClass. These projects are developed by INPE and they produce information on land use and coverage used by the Brazilian Government to make public policy decisions. Besides these projects there are other initiatives from universities and space agencies devoted to the creation of national and global maps.

Although these projects adhere to open data policies and provide a rich collection of data, there are still a gap however in tools that facilitate the integrated use of these collections: it requires from researchers, students and public officials a great effort to collect, organize and integrate all the datasets, prior to their use. In general, each data collection adopts its own land use and cover classification system, with class names and meanings very different across the collections. Besides that, the collections have different spatial and temporal resolutions, rely on different data representation (raster or vector), and are served by different systems in several formats (files, DBMS or web services).

In this context, the Web Land Trajectory Service (WLTS) is a service that aims to facilitate the access to these various land use and cover data collections through a tailored API. The result is tool that allows researchers and specialists to spend their time in the analytical process, once the API provides the integration of these datasets and brings the concept of Land Use and Cover Trajectories as a high level abstraction. The WLTS approach is to use a data model that defines a minimum set of temporal and spatial information to represent different sources and types of data. WLTS can be used in a range of application, such as in validation of land cover data sets, in the selection of trainning samples to support Machine Learning algorithms used in the generation of new classification maps.

For more information on WLTS, see:

• WLTS Specification: the WLTS specification using OpenAPI 3.0 notation.
• WLTS.py: Python Client Library for Web Land Cover Trajectory Service.
• LCCS-WS Specification: which is used to represent the classes associated with the resources retrieved in WLTS queries.
• LCCS.py: Python Client Library for Land Cover Classification System Web Service.
• LCCS Server: Python Web Server for LCCS.

See INSTALL.rst.

See DEPLOY.rst.

一个简单的扫码工具。

• 一个简单的扫码工具。
• 支持闪光灯。
• 可从图库选取识别。
• 可以复制条码。
• 可以保存条码图片。
• 目前支持的格式：UPC A, UPC E, EAN 8, EAN 13, Code 39, Code 93, Code 128, ITF, QR Code。
• 识别到EAN 13时，可以通过亚马逊查询。
• 识别到ISBN时，会调用豆瓣图书api查询图书信息。
• 识别到开头为690-695（中国大陆的商品）的EAN 13时，会调用国家食品（产品）安全追溯平台的api查询商品信息。
• 识别到 http:// 或 https:// 开头的QR Code时可以直接访问。
• 识别到ss://开头的QR Code时可以打开Shadowsocks并添加配置。
• 允许其它应用调用。

CaptureActivty一共注册了三个Action：

<activity
    android:name="com.gooosie.scancode.ui.activity.CaptureActivity"
    android:configChanges="orientation|keyboardHidden"
    android:launchMode="singleTask"
    android:screenOrientation="portrait"
    android:windowSoftInputMode="stateAlwaysHidden" >
    <intent-filter>
        <action android:name="com.gooosie.scancode.SCAN"/>
        <category android:name="android.intent.category.DEFAULT"/>
    </intent-filter>
    <intent-filter>
        <action android:name="com.google.zxing.client.android.SCAN"/>
        <category android:name="android.intent.category.DEFAULT"/>
    </intent-filter>
    <intent-filter>
        <action android:name="android.intent.action.VIEW"/>
        <category android:name="android.intent.category.DEFAULT"/>
        <category android:name="android.intent.category.BROWSABLE"/>
        <data android:scheme="zxing" android:host="scan" android:path="/"/>
    </intent-filter>
</activity>

并在扫码成功时：

@Override
public void onAnalyzeSuccess(Bitmap bitmap, Result result) {
    Intent resultIntent = new Intent();
    Bundle bundle = new Bundle();
    bundle.putInt(CodeUtil.RESULT_TYPE, CodeUtil.RESULT_SUCCESS);
    bundle.putString(Intents.Scan.RESULT, result.getText());
    bundle.putString(Intents.Scan.RESULT_FORMAT, result.getBarcodeFormat().name());
    resultIntent.putExtras(bundle);
    mActivity.setResult(Activity.RESULT_OK, resultIntent);
    mActivity.finish();
}

在扫码失败时：

@Override
public void onAnalyzeFailed() {
    Intent resultIntent = new Intent();
    Bundle bundle = new Bundle();
    bundle.putInt(CodeUtil.RESULT_TYPE, CodeUtil.RESULT_FAILED);
    bundle.putString(Intents.Scan.RESULT, "");
    resultIntent.putExtras(bundle);
    mActivity.setResult(Activity.RESULT_OK, resultIntent);
    mActivity.finish();
}

其中：

CodeUtil.RESULT_TYPE       // "RESULT_TYPE"
CodeUtil.RESULT_SUCCESS    // 1
CodeUtil.RESULT_FAILED     // 2
Intents.Scan.RESULT        // "SCAN_RESULT"
Intents.Scan.RESULT_FORMAT // "SCAN_RESULT_FORMAT"

使用实例：

public void startCaptureActivity() {
    Intent intent = new Intent("com.gooosie.scancode.SCAN");
    startActivityForResult(intent, 100);
}

@Override
protected void onActivityResult(int requestCode, int resultCode, Intent data) {
    if (requestCode == 100 && resultCode == RESULT_OK) {
        Bundle bundle = data.getExtras();
        String format = bundle.getString("SCAN_RESULT_FORMAT");
        String code = bundle.getString("SCAN_RESULT");   
        // ...
    }
}

• ZXing

下载

Apache License 2.0

Assalamu’alaikum

This is a custom integration for Home Assistant that will create a Prayer Time sensor based on Person’s GPS coordinates. The GPS-based prayer time API is provided by Malaysia Prayer Time.

Prayer time information are made as sensor attributes with the following format:

• 12 hours (e.g. 6:01 AM)
• 24 hours (e.g. 06:01:00)
• Datetime UTC (e.g. 2023-07-29T22:01:00+00:00)

This is a continuation of HomeAssistantEsolatGPS (Appdaemon version)

• Added Current Prayer Time (sensor.esolatnow)
• Dynamic Prayer Time dashboard using Markdown card

• Home Assistant 2021.x and above
• Person entity with GPS location (device tracker)

1. Copy the esolatgps directory from custom_components in this repository and place inside your Home Assistant’s custom_components directory.
2. Restart Home Assistant
3. Follow the instructions in the Setup section

The sensors will be populated sensor.esolat_ based on the person with GPS coordinates.

I have prepared a markdown card template that will:

• Automatically show Prayer time based on the logged in user
• If user state is at home the prayer time will automatically switched to Home prayer time

You may refer to the Markdown.md and copy the markdown codes.

• HomeAssistantMalaysia
• Saudara Noorzaini Ilhami for his MPT API
• Prayer times data by JAKIM. Geolocation data by Google

sdk2json is a go package that parses the Windows SDK (function prototypes, structures, unions) to a friendlier format like JSON. The purpose of this tool was initially for the API hooking module (of saferwall sandbox) to allow an implementation of a generic hook handler that does not require knowledge of specific API prototype to know how to log them.

winsdk2json relies on a C compiler frontend for parsing the C headers and convert them to a higher level objects. Those objects can be serialized formats like JSON or YAML.

Here is an example:

{
 "advapi32.dll": {
  "ControlService": {
   "callconv": "WINAPI",
   "name": "ControlService",
   "retVal": "BOOL",
   "params": [
    {
     "anno": "_In_",
     "type": "SC_HANDLE",
     "name": "hService"
    },
    {
     "anno": "_In_",
     "type": "DWORD",
     "name": "dwControl"
    },
    {
     "anno": "_Out_",
     "type": "LPSERVICE_STATUS",
     "name": "lpServiceStatus"
    }
   ]
  },

You can download the JSON files that contains all the definitions of the Win32 APIs in the releases page. If however, you intend to generate those definitions on another custom that fits your needs, please follow the instructions below:

Clone the repo:

git clone https://github.com/saferwall/winsdk2json.git
cd winsdk2json
git submodule init
git submodule update

Build & run”

go build cli.go
.\cli.exe -h

WinSdk2JSON - a tool to parse the Windows Win32 SDK into JSON format.
For more details see the github repo at https://github.com/saferwall/winsdk2json

Usage:
  winsdk2json [flags]
  winsdk2json [command]

Available Commands:
  completion  Generate the autocompletion script for the specified shell
  help        Help about any command
  parse       Walk through the Windows SDK and parse the Win32 headers
  version     Version number

Flags:
  -h, --help   help for winsdk2json

This package tries to stay up to date with the latest Windows SDK, they are copied into the winsdk/ folder. However, feel free to use an SDK version that is not included in this repo. The parse command takes a i argument that points to the Windows SDK headers. For example: C:\\Program Files (x86)\\Windows Kits\\10\\Include\\10.0.19041.0\\.

• SAL annotations
• Some Windows APIs does not have a ANSI alternative: i.e OpenMutexW
• API like CreateToolhelp32Snapshot does not have annotations.

• https://github.com/microsoft/win32metadata
• https://github.com/BehroozAbbassi/sdkffi

Although there are plenty of Gatsby+Hasura and Gatsby+Auth0 tutorials out there, at the time of writing all were out of date and using old patterns with deprecated libraries.

This starter provides a simple Gatsby Admin page that:

1. Authenticates via Auth0 OAuth 2.0 to obtain a JSON Web Token (JWT); and
2. Uses the JWT in the request header to make simple GraphQL query and mutation requests to Hasura.

The Admin page is styled with Bootstrap and is based on a slightly modified Dashboard Component. “Organizations” are used as an example for listing and creating objects/records, all other nav links serve as display placeholders only.

This starter uses:

• TypeScript
• auth0-react for Auth0 OAuth 2.0 authentication with JWTs
• urgql for the GraphQL client
• react-bootstrap and react-feather for styling

1. Follow instructions to set up Hasura
2. Create a simple table to store Organizations

  CREATE TABLE organizations(
    id SERIAL PRIMARY KEY,
    name TEXT,
    label TEXT
  );

2. Track the table from the Hasura console Data tab
3. Add a few test records from the Hasura console Data > Insert Row tab
4. Allow your role (configured below) to insert and select from the Hasura console Data > Permissions tab

Follow these steps to set up Auth0 with custom claims for Hasura.

Rename .env.example to .env.development and update accordingly

Please help us keep this starter up to date and adhering to best practices and conventions. We are not veterans in the space and appreciate any contributions or suggestions on how things can be done better. Feel free to submit an issue, pull request or reach out: hello at whitebrick dot com