{
  "id": 131201,
  "title": "Anomaly Detection in Satellite imagery 📶",
  "url": "/competitions/ds4g-environmental-insights-explorer/discussion/131201",
  "author_name": "",
  "post_date": "2020-02-18T20:05:19.030971700Z",
  "votes": 16,
  "comment_count": 18,
  "views": 0,
  "content": "<p></p>\n\n<h1>For everyone’s reference. I already separated a material for this great competition! Going to be adding here more as I keep searching!</h1>\n\n<p>Animations</p>\n\n<h2>Papers</h2>\n\n<p><a href=\"https://www3.epa.gov/ttn/chief/efpac/procedures/procedures81213.pdf\">Recommended Procedures for Development of\nEmissions Factors and Use of the WebFIRE\nDatabase -PG 77 outlier</a></p>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3595464%2Fc5b9f690569b6df053dea220c92b0312%2Fmd.PNG?generation=1582056201235364&amp;alt=media\" alt=\"\"></p>\n\n<ul>\n<li><a href=\"https://www.atmos-meas-tech-discuss.net/amt-2019-249/amt-2019-249.pdf\">Automatic Quality Control of the Meteosat First Generation\nMeasurements\n</a></li>\n</ul>\n\n<p>-[Finding and Detection of Outlier Regions in Satellite ](Image <a href=\"http://www.ipcsit.com/vol11/1-ICNEE2011-N002.pdf\">http://www.ipcsit.com/vol11/1-ICNEE2011-N002.pdf</a>)</p>\n\n<h1>Useful Links - Outlier detection</h1>\n\n<ul>\n<li><a href=\"https://plot.ly/python/v3/outlier-test/\">dixon test outliers python</a></li>\n<li><a href=\"https://www.hs.uni-hamburg.de/DE/Ins/Per/Czesla/PyA/PyA/pyaslDoc/aslDoc/outlier.html\">Rosner's test outlier python</a></li>\n<li><a href=\"https://www.researchgate.net/publication/283476235_Comparison_of_Methods_for_detecting_Outliers\">Comparison of Methods for detecting Outliers</a></li>\n</ul>\n\n<h2>Useful Links</h2>\n\n<ul>\n<li><a href=\"https://www.satimagingcorp.com/satellite-sensors/\">Satellite Sensors (0.31m - 2m</a></li>\n<li><a href=\"https://towardsdatascience.com/how-to-compute-satellite-image-statistics-and-use-it-in-pandas-81864a489144\">How To Compute Satellite Image Statistics</a>\n-<a href=\"https://towardsdatascience.com/satellite-imagery-access-and-analysis-in-python-jupyter-notebooks-387971ece84b\">Satellite imagery access and analysis in Python &amp; Jupyter notebooks</a></li>\n</ul>\n\n<p>-<a href=\"https://www.researchgate.net/topic/Raster-Image\">60 questions about Raster Image </a></p>\n\n<h2>Videos</h2>\n\n<ul>\n<li><a href=\"https://youtu.be/b8cDu5CoLVU?t=20\">Automated anomaly detection in satellite imagery</a></li>\n<li><a href=\"https://youtu.be/DafZSeIGLNE\">Combining satellite imagery and machine learning to predict poverty</a></li>\n</ul>\n\n<h3>Useful Videos</h3>\n\n<ul>\n<li><a href=\"https://youtu.be/F7dM5vmMqdk\">Deep Learning and the Analysis of Satellite Imagery</a>\n Orbital Insight is a Geospatial Big Data company leveraging the rapidly growing availability of satellite, UAV, and other geospatial data sources, to understand and characterize socio-economic trends at global, regional, and hyper-local scales.  In this talk Boris discusses the satellite imagery domain, how it’s evolving, and the various advantages and challenges of working with such imagery.  </li>\n</ul>\n\n<h2>Libs</h2>\n\n<ul>\n<li><a href=\"https://github.com/robmarkcole/satellite-image-deep-learning\">Resources for deep learning with satellite &amp; aerial imagery</a>\n-<a href=\"https://github.com/deepVector/geospatial-machine-learning\">A curated list of resources focused on Machine Learning in Geospatial Data Science</a>\n-<a href=\"https://gist.github.com/jacquestardie/0d1c0cb413b3b9b06edf\">Libraries for a modern geospatial workflow</a></li>\n</ul>\n\n<p>👍 ✔️ Thanks Everyone✔️</p>",
  "messages": [
    {
      "id": "749610",
      "postDate": "02/18/2020 20:05:19",
      "content": "<p></p>\n\n<h1>For everyone’s reference. I already separated a material for this great competition! Going to be adding here more as I keep searching!</h1>\n\n<p>Animations</p>\n\n<h2>Papers</h2>\n\n<p><a href=\"https://www3.epa.gov/ttn/chief/efpac/procedures/procedures81213.pdf\">Recommended Procedures for Development of\nEmissions Factors and Use of the WebFIRE\nDatabase -PG 77 outlier</a></p>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3595464%2Fc5b9f690569b6df053dea220c92b0312%2Fmd.PNG?generation=1582056201235364&amp;alt=media\" alt=\"\"></p>\n\n<ul>\n<li><a href=\"https://www.atmos-meas-tech-discuss.net/amt-2019-249/amt-2019-249.pdf\">Automatic Quality Control of the Meteosat First Generation\nMeasurements\n</a></li>\n</ul>\n\n<p>-[Finding and Detection of Outlier Regions in Satellite ](Image <a href=\"http://www.ipcsit.com/vol11/1-ICNEE2011-N002.pdf\">http://www.ipcsit.com/vol11/1-ICNEE2011-N002.pdf</a>)</p>\n\n<h1>Useful Links - Outlier detection</h1>\n\n<ul>\n<li><a href=\"https://plot.ly/python/v3/outlier-test/\">dixon test outliers python</a></li>\n<li><a href=\"https://www.hs.uni-hamburg.de/DE/Ins/Per/Czesla/PyA/PyA/pyaslDoc/aslDoc/outlier.html\">Rosner's test outlier python</a></li>\n<li><a href=\"https://www.researchgate.net/publication/283476235_Comparison_of_Methods_for_detecting_Outliers\">Comparison of Methods for detecting Outliers</a></li>\n</ul>\n\n<h2>Useful Links</h2>\n\n<ul>\n<li><a href=\"https://www.satimagingcorp.com/satellite-sensors/\">Satellite Sensors (0.31m - 2m</a></li>\n<li><a href=\"https://towardsdatascience.com/how-to-compute-satellite-image-statistics-and-use-it-in-pandas-81864a489144\">How To Compute Satellite Image Statistics</a>\n-<a href=\"https://towardsdatascience.com/satellite-imagery-access-and-analysis-in-python-jupyter-notebooks-387971ece84b\">Satellite imagery access and analysis in Python &amp; Jupyter notebooks</a></li>\n</ul>\n\n<p>-<a href=\"https://www.researchgate.net/topic/Raster-Image\">60 questions about Raster Image </a></p>\n\n<h2>Videos</h2>\n\n<ul>\n<li><a href=\"https://youtu.be/b8cDu5CoLVU?t=20\">Automated anomaly detection in satellite imagery</a></li>\n<li><a href=\"https://youtu.be/DafZSeIGLNE\">Combining satellite imagery and machine learning to predict poverty</a></li>\n</ul>\n\n<h3>Useful Videos</h3>\n\n<ul>\n<li><a href=\"https://youtu.be/F7dM5vmMqdk\">Deep Learning and the Analysis of Satellite Imagery</a>\n Orbital Insight is a Geospatial Big Data company leveraging the rapidly growing availability of satellite, UAV, and other geospatial data sources, to understand and characterize socio-economic trends at global, regional, and hyper-local scales.  In this talk Boris discusses the satellite imagery domain, how it’s evolving, and the various advantages and challenges of working with such imagery.  </li>\n</ul>\n\n<h2>Libs</h2>\n\n<ul>\n<li><a href=\"https://github.com/robmarkcole/satellite-image-deep-learning\">Resources for deep learning with satellite &amp; aerial imagery</a>\n-<a href=\"https://github.com/deepVector/geospatial-machine-learning\">A curated list of resources focused on Machine Learning in Geospatial Data Science</a>\n-<a href=\"https://gist.github.com/jacquestardie/0d1c0cb413b3b9b06edf\">Libraries for a modern geospatial workflow</a></li>\n</ul>\n\n<p>👍 ✔️ Thanks Everyone✔️</p>",
      "rawMarkdown": "![](https://miro.medium.com/max/1110/1*Vv1-yCsyKUhL44IPZvjPrQ.jpeg)\n# For everyone’s reference. I already separated a material for this great competition! Going to be adding here more as I keep searching!\nAnimations\n\n## Papers\n\n[Recommended Procedures for Development of\nEmissions Factors and Use of the WebFIRE\nDatabase -PG 77 outlier](https://www3.epa.gov/ttn/chief/efpac/procedures/procedures81213.pdf)\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3595464%2Fc5b9f690569b6df053dea220c92b0312%2Fmd.PNG?generation=1582056201235364&amp;alt=media)\n\n- [Automatic Quality Control of the Meteosat First Generation\nMeasurements\n](https://www.atmos-meas-tech-discuss.net/amt-2019-249/amt-2019-249.pdf)\n\n-[Finding and Detection of Outlier Regions in Satellite ](Image http://www.ipcsit.com/vol11/1-ICNEE2011-N002.pdf)\n\n# Useful Links - Outlier detection\n- [dixon test outliers python](https://plot.ly/python/v3/outlier-test/)\n- [Rosner's test outlier python](https://www.hs.uni-hamburg.de/DE/Ins/Per/Czesla/PyA/PyA/pyaslDoc/aslDoc/outlier.html)\n- [Comparison of Methods for detecting Outliers](https://www.researchgate.net/publication/283476235_Comparison_of_Methods_for_detecting_Outliers)\n\n## Useful Links\n- [Satellite Sensors (0.31m - 2m](https://www.satimagingcorp.com/satellite-sensors/)\n- [How To Compute Satellite Image Statistics](https://towardsdatascience.com/how-to-compute-satellite-image-statistics-and-use-it-in-pandas-81864a489144)\n-[Satellite imagery access and analysis in Python &amp; Jupyter notebooks](https://towardsdatascience.com/satellite-imagery-access-and-analysis-in-python-jupyter-notebooks-387971ece84b)\n\n-[60 questions about Raster Image ](https://www.researchgate.net/topic/Raster-Image)\n\n## Videos\n- [Automated anomaly detection in satellite imagery](https://youtu.be/b8cDu5CoLVU?t=20)\n- [Combining satellite imagery and machine learning to predict poverty](https://youtu.be/DafZSeIGLNE)\n    \n\n### Useful Videos\n- [Deep Learning and the Analysis of Satellite Imagery](https://youtu.be/F7dM5vmMqdk)\n     Orbital Insight is a Geospatial Big Data company leveraging the rapidly growing availability of satellite, UAV, and other geospatial data sources, to understand and characterize socio-economic trends at global, regional, and hyper-local scales.  In this talk Boris discusses the satellite imagery domain, how it’s evolving, and the various advantages and challenges of working with such imagery.  \n\n## Libs\n- [Resources for deep learning with satellite &amp; aerial imagery](https://github.com/robmarkcole/satellite-image-deep-learning)\n-[A curated list of resources focused on Machine Learning in Geospatial Data Science](https://github.com/deepVector/geospatial-machine-learning)\n-[Libraries for a modern geospatial workflow](https://gist.github.com/jacquestardie/0d1c0cb413b3b9b06edf)\n\n\n👍 ✔️ Thanks Everyone✔️",
      "votes": null
    },
    {
      "id": "750560",
      "postDate": "02/19/2020 14:01:46",
      "content": "<p>Thanks for Sharing</p>",
      "rawMarkdown": "Thanks for Sharing",
      "votes": null
    },
    {
      "id": "751094",
      "postDate": "02/20/2020 01:52:38",
      "content": "<p>Thanks <a href=\"/aravindhkv123\">@aravindhkv123</a> Waiting your great notebook for this comp</p>",
      "rawMarkdown": "Thanks @aravindhkv123 Waiting your great notebook for this comp",
      "votes": null
    },
    {
      "id": "751099",
      "postDate": "02/20/2020 01:54:50",
      "content": "<p>If you like outlier detection please consider see this topic:\n<a href=\"https://www.kaggle.com/general/128356\">The top 57 Anomaly Detection Open Source Projects</a></p>",
      "rawMarkdown": "If you like outlier detection please consider see this topic:\n[The top 57 Anomaly Detection Open Source Projects](https://www.kaggle.com/general/128356)",
      "votes": null
    },
    {
      "id": "751200",
      "postDate": "02/20/2020 04:13:57",
      "content": "<p>Great resources.</p>\n\n<p>Thank u for sharing.</p>",
      "rawMarkdown": "Great resources.\n\nThank u for sharing.",
      "votes": null
    },
    {
      "id": "751432",
      "postDate": "02/20/2020 07:36:41",
      "content": "<p>cool</p>",
      "rawMarkdown": "cool",
      "votes": null
    },
    {
      "id": "752192",
      "postDate": "02/20/2020 19:32:43",
      "content": "<p>Nice</p>",
      "rawMarkdown": "Nice",
      "votes": null
    },
    {
      "id": "752279",
      "postDate": "02/20/2020 20:42:19",
      "content": "<p>Thanks <a href=\"/brycewayne\">@brycewayne</a> ! have a good day !</p>",
      "rawMarkdown": "Thanks @brycewayne ! have a good day !",
      "votes": null
    },
    {
      "id": "752280",
      "postDate": "02/20/2020 20:42:37",
      "content": "<p>Thanks ! Waiting for your work in this comp!</p>",
      "rawMarkdown": "Thanks ! Waiting for your work in this comp!",
      "votes": null
    },
    {
      "id": "752281",
      "postDate": "02/20/2020 20:43:14",
      "content": "<p>Thanks ! pls share some topic about  anomaly detection ! have a good day!</p>",
      "rawMarkdown": "Thanks ! pls share some topic about  anomaly detection ! have a good day!",
      "votes": null
    },
    {
      "id": "753078",
      "postDate": "02/21/2020 17:25:00",
      "content": "<p>wow , great  write up awesome !! 🙂 🙂 </p>",
      "rawMarkdown": "wow , great  write up awesome !! 🙂 🙂",
      "votes": null
    },
    {
      "id": "753892",
      "postDate": "02/22/2020 19:34:37",
      "content": "<p>Great Content !</p>",
      "rawMarkdown": "Great Content !",
      "votes": null
    },
    {
      "id": "753955",
      "postDate": "02/22/2020 21:19:59",
      "content": "<p>Thanks <a href=\"/mielek\">@mielek</a> ! have a good day !</p>",
      "rawMarkdown": "Thanks @mielek ! have a good day !",
      "votes": null
    },
    {
      "id": "753956",
      "postDate": "02/22/2020 21:20:21",
      "content": "<p>keep going bro ! pls share your insights here !</p>",
      "rawMarkdown": "keep going bro ! pls share your insights here !",
      "votes": null
    },
    {
      "id": "754184",
      "postDate": "02/23/2020 07:17:11",
      "content": "<p>Have a god day,too <a href=\"/caesarlupum\">@caesarlupum</a> </p>",
      "rawMarkdown": "Have a god day,too @caesarlupum",
      "votes": null
    },
    {
      "id": "754223",
      "postDate": "02/23/2020 08:32:48",
      "content": "<p>Great</p>",
      "rawMarkdown": "Great",
      "votes": null
    },
    {
      "id": "754358",
      "postDate": "02/23/2020 13:08:54",
      "content": "<p><a href=\"/charandeepsingh1\">@charandeepsingh1</a>, thanks ! Feel free and share useful here !</p>\n\n<p>Have a good day buddy ! </p>",
      "rawMarkdown": "charandeepsingh1, thanks ! Feel free and share useful here !\n\nHave a good day buddy !",
      "votes": null
    },
    {
      "id": "757568",
      "postDate": "02/26/2020 23:06:52",
      "content": "<p>page 77 about outlier: <a href=\"https://www3.epa.gov/ttn/chief/efpac/procedures/procedures81213.pdf\">https://www3.epa.gov/ttn/chief/efpac/procedures/procedures81213.pdf</a></p>",
      "rawMarkdown": "page 77 about outlier: https://www3.epa.gov/ttn/chief/efpac/procedures/procedures81213.pdf",
      "votes": null
    },
    {
      "id": "760913",
      "postDate": "03/01/2020 22:17:44",
      "content": "<p><a href=\"/deepakdeepu8978\">@deepakdeepu8978</a>, this is my  <a href=\"https://www.kaggle.com/caesarlupum/ds4g-autoregressive-integrated-moving\">outlier approach</a>, working in the process.</p>",
      "rawMarkdown": "deepakdeepu8978, this is my  [outlier approach](https://www.kaggle.com/caesarlupum/ds4g-autoregressive-integrated-moving), working in the process.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 750560,
      "author_name": "aravindhkv123",
      "author_url": "",
      "post_date": "02/19/2020 14:01:46",
      "content": "<p>Thanks for Sharing</p>",
      "votes": null,
      "replies": [
        {
          "id": 751094,
          "author_name": "caesarlupum",
          "author_url": "",
          "post_date": "02/20/2020 01:52:38",
          "content": "<p>Thanks <a href=\"/aravindhkv123\">@aravindhkv123</a> Waiting your great notebook for this comp</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 751099,
          "author_name": "caesarlupum",
          "author_url": "",
          "post_date": "02/20/2020 01:54:50",
          "content": "<p>If you like outlier detection please consider see this topic:\n<a href=\"https://www.kaggle.com/general/128356\">The top 57 Anomaly Detection Open Source Projects</a></p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 751200,
      "author_name": "prashant111",
      "author_url": "",
      "post_date": "02/20/2020 04:13:57",
      "content": "<p>Great resources.</p>\n\n<p>Thank u for sharing.</p>",
      "votes": null,
      "replies": [
        {
          "id": 752281,
          "author_name": "caesarlupum",
          "author_url": "",
          "post_date": "02/20/2020 20:43:14",
          "content": "<p>Thanks ! pls share some topic about  anomaly detection ! have a good day!</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 751432,
      "author_name": "",
      "author_url": "",
      "post_date": "02/20/2020 07:36:41",
      "content": "<p>cool</p>",
      "votes": null,
      "replies": [
        {
          "id": 752280,
          "author_name": "caesarlupum",
          "author_url": "",
          "post_date": "02/20/2020 20:42:37",
          "content": "<p>Thanks ! Waiting for your work in this comp!</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 752192,
      "author_name": "",
      "author_url": "",
      "post_date": "02/20/2020 19:32:43",
      "content": "<p>Nice</p>",
      "votes": null,
      "replies": [
        {
          "id": 752279,
          "author_name": "caesarlupum",
          "author_url": "",
          "post_date": "02/20/2020 20:42:19",
          "content": "<p>Thanks <a href=\"/brycewayne\">@brycewayne</a> ! have a good day !</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 753078,
      "author_name": "deepakdeepu8978",
      "author_url": "",
      "post_date": "02/21/2020 17:25:00",
      "content": "<p>wow , great  write up awesome !! 🙂 🙂 </p>",
      "votes": null,
      "replies": [
        {
          "id": 753956,
          "author_name": "caesarlupum",
          "author_url": "",
          "post_date": "02/22/2020 21:20:21",
          "content": "<p>keep going bro ! pls share your insights here !</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 760913,
          "author_name": "caesarlupum",
          "author_url": "",
          "post_date": "03/01/2020 22:17:44",
          "content": "<p><a href=\"/deepakdeepu8978\">@deepakdeepu8978</a>, this is my  <a href=\"https://www.kaggle.com/caesarlupum/ds4g-autoregressive-integrated-moving\">outlier approach</a>, working in the process.</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 753892,
      "author_name": "mielek",
      "author_url": "",
      "post_date": "02/22/2020 19:34:37",
      "content": "<p>Great Content !</p>",
      "votes": null,
      "replies": [
        {
          "id": 753955,
          "author_name": "caesarlupum",
          "author_url": "",
          "post_date": "02/22/2020 21:19:59",
          "content": "<p>Thanks <a href=\"/mielek\">@mielek</a> ! have a good day !</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 754184,
          "author_name": "mielek",
          "author_url": "",
          "post_date": "02/23/2020 07:17:11",
          "content": "<p>Have a god day,too <a href=\"/caesarlupum\">@caesarlupum</a> </p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 754223,
      "author_name": "",
      "author_url": "",
      "post_date": "02/23/2020 08:32:48",
      "content": "<p>Great</p>",
      "votes": null,
      "replies": [
        {
          "id": 754358,
          "author_name": "caesarlupum",
          "author_url": "",
          "post_date": "02/23/2020 13:08:54",
          "content": "<p><a href=\"/charandeepsingh1\">@charandeepsingh1</a>, thanks ! Feel free and share useful here !</p>\n\n<p>Have a good day buddy ! </p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 757568,
          "author_name": "caesarlupum",
          "author_url": "",
          "post_date": "02/26/2020 23:06:52",
          "content": "<p>page 77 about outlier: <a href=\"https://www3.epa.gov/ttn/chief/efpac/procedures/procedures81213.pdf\">https://www3.epa.gov/ttn/chief/efpac/procedures/procedures81213.pdf</a></p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "749610": "![](https://miro.medium.com/max/1110/1*Vv1-yCsyKUhL44IPZvjPrQ.jpeg)\n# For everyone’s reference. I already separated a material for this great competition! Going to be adding here more as I keep searching!\nAnimations\n\n## Papers\n\n[Recommended Procedures for Development of\nEmissions Factors and Use of the WebFIRE\nDatabase -PG 77 outlier](https://www3.epa.gov/ttn/chief/efpac/procedures/procedures81213.pdf)\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3595464%2Fc5b9f690569b6df053dea220c92b0312%2Fmd.PNG?generation=1582056201235364&amp;alt=media)\n\n- [Automatic Quality Control of the Meteosat First Generation\nMeasurements\n](https://www.atmos-meas-tech-discuss.net/amt-2019-249/amt-2019-249.pdf)\n\n-[Finding and Detection of Outlier Regions in Satellite ](Image http://www.ipcsit.com/vol11/1-ICNEE2011-N002.pdf)\n\n# Useful Links - Outlier detection\n- [dixon test outliers python](https://plot.ly/python/v3/outlier-test/)\n- [Rosner's test outlier python](https://www.hs.uni-hamburg.de/DE/Ins/Per/Czesla/PyA/PyA/pyaslDoc/aslDoc/outlier.html)\n- [Comparison of Methods for detecting Outliers](https://www.researchgate.net/publication/283476235_Comparison_of_Methods_for_detecting_Outliers)\n\n## Useful Links\n- [Satellite Sensors (0.31m - 2m](https://www.satimagingcorp.com/satellite-sensors/)\n- [How To Compute Satellite Image Statistics](https://towardsdatascience.com/how-to-compute-satellite-image-statistics-and-use-it-in-pandas-81864a489144)\n-[Satellite imagery access and analysis in Python &amp; Jupyter notebooks](https://towardsdatascience.com/satellite-imagery-access-and-analysis-in-python-jupyter-notebooks-387971ece84b)\n\n-[60 questions about Raster Image ](https://www.researchgate.net/topic/Raster-Image)\n\n## Videos\n- [Automated anomaly detection in satellite imagery](https://youtu.be/b8cDu5CoLVU?t=20)\n- [Combining satellite imagery and machine learning to predict poverty](https://youtu.be/DafZSeIGLNE)\n    \n\n### Useful Videos\n- [Deep Learning and the Analysis of Satellite Imagery](https://youtu.be/F7dM5vmMqdk)\n     Orbital Insight is a Geospatial Big Data company leveraging the rapidly growing availability of satellite, UAV, and other geospatial data sources, to understand and characterize socio-economic trends at global, regional, and hyper-local scales.  In this talk Boris discusses the satellite imagery domain, how it’s evolving, and the various advantages and challenges of working with such imagery.  \n\n## Libs\n- [Resources for deep learning with satellite &amp; aerial imagery](https://github.com/robmarkcole/satellite-image-deep-learning)\n-[A curated list of resources focused on Machine Learning in Geospatial Data Science](https://github.com/deepVector/geospatial-machine-learning)\n-[Libraries for a modern geospatial workflow](https://gist.github.com/jacquestardie/0d1c0cb413b3b9b06edf)\n\n\n👍 ✔️ Thanks Everyone✔️",
    "750560": "Thanks for Sharing",
    "751094": "Thanks @aravindhkv123 Waiting your great notebook for this comp",
    "751099": "If you like outlier detection please consider see this topic:\n[The top 57 Anomaly Detection Open Source Projects](https://www.kaggle.com/general/128356)",
    "751200": "Great resources.\n\nThank u for sharing.",
    "751432": "cool",
    "752192": "Nice",
    "752279": "Thanks @brycewayne ! have a good day !",
    "752280": "Thanks ! Waiting for your work in this comp!",
    "752281": "Thanks ! pls share some topic about  anomaly detection ! have a good day!",
    "753078": "wow , great  write up awesome !! 🙂 🙂",
    "753892": "Great Content !",
    "753955": "Thanks @mielek ! have a good day !",
    "753956": "keep going bro ! pls share your insights here !",
    "754184": "Have a god day,too @caesarlupum",
    "754223": "Great",
    "754358": "charandeepsingh1, thanks ! Feel free and share useful here !\n\nHave a good day buddy !",
    "757568": "page 77 about outlier: https://www3.epa.gov/ttn/chief/efpac/procedures/procedures81213.pdf",
    "760913": "deepakdeepu8978, this is my  [outlier approach](https://www.kaggle.com/caesarlupum/ds4g-autoregressive-integrated-moving), working in the process."
  },
  "source": "meta"
}