Livestock census (village level)

[kapildadheech]

Ticket Contents

Extent of different livestock types (village), Obtain data, add as vector map, then use GEE to produce vectorized output

Goals

Goals

• Obtain data
• dd as vector map, then use GEE to produce vectorized output

Expected Outcome

Extent of different livestock types (village)

Acceptance Criteria

Data Acquisition

• Given the livestock census/village-level dataset is accessible, when the data is obtained, then it must include extent/counts for different livestock types (e.g., cattle, buffalo, goat, sheep, poultry).
• The dataset must include unique village identifiers (e.g., village code/name) for spatial join with village boundaries.
• Data must be exported in a structured format (CSV/GeoJSON) suitable for further processing.

Vector Map Creation

• Given the livestock dataset and village boundary geometry, when the processing script runs, then each village polygon must be enriched with livestock attributes.
• The resulting vector map must have valid geometries, standardized CRS (EPSG:4326), and consistent attribute schema.
• The map must be published as a vector asset in Earth Engine with metadata (source, date, livestock types included).

GEE Vectorized Output

• Given the published vector asset, when the GEE pipeline runs, then it must produce a vectorized map showing livestock extent at the village level.
• The output must allow filtering/aggregation by livestock type.

Implementation Details

Implement in python notebook and submit or write in computing module

Mockups/Wireframes

No response

Product Name

Livestock census (village level)

Organisation Name

C4GT

Domain

No response

Tech Skills Needed

Python

Organizational Mentor

@amanodt @kapildadheech @ankit-work7

Angel Mentor

No response

Complexity

Medium

Category

Backend

[HarK-github]

Hi @kapildadheech and mentors 👋

I’m interested in contributing to the Livestock census (village level) task and would like to take this up.
Before starting implementation, I had a few clarifications to ensure alignment:

• Livestock census data source:
  Is there a preferred/official dataset (and census year) for village-level livestock data, or should contributors independently identify and validate a suitable source?
• Village boundary dataset:
  Should we use an existing village boundary dataset already used in the project, or is it acceptable to source an external authoritative dataset (e.g., Census of India village boundaries)?
• Geographic scope:
  Is the expected output pan-India, or limited to a specific state/district for the initial implementation?
• GEE asset ownership:
  Should the final vector asset be published under a shared project/org account, or under the contributor’s GEE account and later transferred?

[amanodt]

Hi, We've been having weekly dev calls on Fridays at 3 pm. Will be good to join these calls and discuss there too. Details here: https://core-stack.org/core-stack-innovation-challenge-1st-edition/ Join the Googlegroup <https://groups.google.com/u/2/g/core-stack-dev> and Discord channel <https://discord.gg/FpMArraM2X>. Please find my response below: - Livestock census data source: Is there a preferred/official dataset (and census year) for village-level livestock data, or should contributors independently identify and validate a suitable source? *-The contributor should identify.* - Village boundary dataset: Should we use an existing village boundary dataset already used in the project, or is it acceptable to source an external authoritative dataset (e.g., Census of India village boundaries)? *-We have mentioned some pan-India assets in the sheet below; you can refer to the village boundaries there.* *https://docs.google.com/spreadsheets/d/1xS5d7vgyjyoqqnmmajKDZBx9qS6GqyAdSbNDR62ot2Y/edit?gid=0#gid=0 <https://docs.google.com/spreadsheets/d/1xS5d7vgyjyoqqnmmajKDZBx9qS6GqyAdSbNDR62ot2Y/edit?gid=0#gid=0>* - Geographic scope: Is the expected output pan-India, or limited to a specific state/district for the initial implementation? *-Pan India is preferred, but it's okay to start with the state/district.* - GEE asset ownership: Should the final vector asset be published under a shared project/org account, or under the contributor’s GEE account and later transferred? *- You can initially keep them under your own GEE account. Once validated and approved, it can then be moved.* Thanks and Regards Aman Verma Lead Engineer CoRE Stack

…

On Fri, Jan 9, 2026 at 3:44 PM HarK ***@***.***> wrote: *HarK-github* left a comment (core-stack-org/core-stack-backend#222) <#222 (comment)> Hi @kapildadheech <https://github.com/kapildadheech> and mentors 👋 I’m interested in contributing to the Livestock census (village level) task and would like to take this up. Before starting implementation, I had a few clarifications to ensure alignment: - Livestock census data source: Is there a preferred/official dataset (and census year) for village-level livestock data, or should contributors independently identify and validate a suitable source? - Village boundary dataset: Should we use an existing village boundary dataset already used in the project, or is it acceptable to source an external authoritative dataset (e.g., Census of India village boundaries)? - Geographic scope: Is the expected output pan-India, or limited to a specific state/district for the initial implementation? - GEE asset ownership: Should the final vector asset be published under a shared project/org account, or under the contributor’s GEE account and later transferred? — Reply to this email directly, view it on GitHub <#222 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BPT2H62LBMQ3YVKWBKH7YKT4F55P5AVCNFSM6AAAAACG27HFSSVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTOMRYGI3DCNZWG4> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[adhiraj0905]

.Hello,

I’m interested in taking this issue up and would like to know if it can be assigned to me. I’m actively learning the required stack and would appreciate any guidance or pointers before starting.

Thank you.

[amanodt]

Hi @adhiraj0905,

@HarK-github is already working on this; maybe you folks can team up to work on this.

Additionally, we've been holding weekly dev calls on Fridays at 3 pm. It would be good to join these calls. Details here: https://core-stack.org/core-stack-innovation-challenge-1st-edition/

Join the Googlegroup https://groups.google.com/u/2/g/core-stack-dev and Discord channel https://discord.gg/FpMArraM2X.

[aaditeshwar]

Hi @HarK-github , We've been having weekly calls to discuss various ongoing contributions. Can you join the next call and also share if you have been able to identify any data sources that have published the livestock census. Please do go over the recordings https://core-stack.org/core-stack-innovation-challenge-1st-edition/ and join the Google group https://groups.google.com/u/2/g/core-stack-dev and discord channel https://discord.gg/FpMArraM2X. We'll meet next Friday at 3pm. Please let us know if you can join.

[atrip0305]

Hi @amanodt @kapildadheech @ankit-work7
I’d love to take up the Livestock Census (village level) task. I have experience working with Python for data preprocessing, structured dataset handling, and building clean, reproducible pipelines.

I’m comfortable with CSV/GeoJSON processing, attribute enrichment, and preparing standardized outputs (CRS handling, schema consistency) for downstream systems like GEE.
Could you please assign this issue to me?
Looking forward to contributing!

[aaditeshwar]

@atrip0305 please do join the weekly dev call on Fridays at 3pm and discuss before you begin, there are some details we'd like to highlight so that your work can be aligned. https://core-stack.org/core-stack-innovation-challenge-1st-edition/

[amit-spatial]

Hi all,

We'll have our dev call at 3pm on Friday as usual. There has also been a lot of interest on some issues we had created on github to add new datasets and pipelines to the CoRE stack. So we wanted to put out some guidelines to make this experience enriching and productive for all of us.

Many of the issues we had created and a wishlist of datasets we had published some time back are very domain specific and need to be technically correct before they can be included in the stack. For context, any of the layers currently on the CoRE stack that required some kind of geospatial algorithms or machine learning went through several months of testing, sometimes years, to examine the outputs carefully, understand when and where the models are performing well or not, how to handle missing data (satellite data can have cloud cover and needs specific methods to address this), and so on.

So if you are keen to pick up such issues then please share your intermediate progress with us during the dev calls. Help us understand how you are approaching the issue, show us the results, discuss problems, and it'll become much easier then to absorb your contributions. If you can inform us in advance about anything specific you'd like to discuss then we can also arrange for somebody who understands that area really well to also come on the call. Amongst so many research scholars and students who have contributed to the CoRE stack, there is a lot of expertise on LULC, computations on DEM layers, tree cover analysis, rainfall, drought, indexes like NDVI, census data, commodity pricing data, agriculture marketing, etc.

We are working hard to put up a developer website asap with all required documentation in one place, but here are two MUST read links before you begin to take up any substantial tasks.

Contribute datasets, pipelines and tools

Core-Stack Innovation Challenge

@HarK-github @atrip0305 See you in today's developer call!

Zoom Link
Meeting ID: 856 5729 0380
Passcode: 317250

[shrutisachan08]

Hi @kapildadheech ,
My approach would be:
Acquire and clean the livestock census dataset with village-level identifiers.
Join the dataset with village boundary shapefiles to create an enriched vector map.
Standardize the data (CRS: EPSG:4326) and ensure consistent attribute schema.
Upload the vector dataset as an Earth Engine asset.
Use GEE to enable filtering and aggregation by livestock type for visualization and analysis.
Could you please confirm the preferred data source and village boundary dataset to use?
If this issue is not taken and not already worked on , I would like to take this up.

[amit-spatial]

Hi @shrutisachan08: Have you found some village level data source?

[shrutisachan08]

Hi @amit-spatial ,
I’m currently exploring available village-level livestock census datasets and suitable boundary shapefiles. Haven’t finalized a reliable source yet, but I’m actively looking into a few options.
I’ll share a confirmed data source and update you by today evening.

[shrutisachan08]

Hi @amit-spatial ,
Hi @kapildadheech,
I’ve identified a village-level livestock dataset from the Livestock Census (DAHD) and village boundary shapefiles from Survey of India for Madhya Pradesh.
Both datasets are currently in raw format, and I’m proceeding with cleaning and aligning village identifiers for spatial joining.
Could you please confirm if these sources are fine to proceed with, or if there’s any preferred dataset I should use instead?

https://docs.google.com/spreadsheets/d/1n28qvSyPmcoBIojP3gtZtxmDtFXzhKuw/edit?usp=sharing&ouid=106377288032683608827&rtpof=true&sd=true

https://surveyofindia.gov.in/documents/MADHYA_PRADESH.zip

[amit-spatial]

Hi @shrutisachan08,

The google docs file fails to open everytime with an error File could not open. Try refreshing the page. Please make sure the file is publicly accessible, so that we can analyse the data.

If possible and directly downloadable, can you please share a direct public resource link or sample data (header + top 5 rows + some rough basic analysis)?

We have found a source for pan India village-ward level data set across all (35) states.

Village and Ward Level Data [Male & Female] - 20th LSC	-	2020-03-31	VillageAndWardLevelDataMale-Female.xlsx

The data doesn't contain village census2011 or lgd ids, so we would need to do a fuzzy matching to map all of it to our dataset. However, if we want to get ids till district level directly and all sheets have been consolidated and harmonised to single csv file, we can directly utilise publicly available ArtPark (IISc) preprocessed dataset.

I have found a source for pan India village-ward level data set across all (35) states.

Village and Ward Level Data [Male & Female] - 20th LSC - 2020-03-31 VillageAndWardLevelDataMale-Female.xlsx

It contains data in 4 sheets, in 4 combinations of Male/Female, Rural/Urban.

Sample:

Rural Male Population

state_name 	district_name 	block_name 	village_name 	cattle 	buffalo 	sheep 	goat 	pig
Andaman And Nicobar Islands 	Nicobars 	Car Nicobar 	Arong 	2 	0 	0 	345 	1256
Andaman And Nicobar Islands 	Nicobars 	Car Nicobar 	Big Lapati 	6 	0 	0 	82 	4184

Rural Female Population

state_name 	district_name 	block_name 	village_name 	cattle 	buffalo 	sheep 	goat 	pig
Andaman And Nicobar Islands 	Nicobars 	Car Nicobar 	Arong 	5 	0 	0 	632 	1793
Andaman And Nicobar Islands 	Nicobars 	Car Nicobar 	Big Lapati 	16 	0 	0 	224 	662

Urban Male Population

state_name 	district_name 	town_name 	ward_name 	cattle 	buffalo 	sheep 	goat 	pig
Andaman And Nicobar Islands 	South Andamans 	Port Blair 	Port Blair (M CI)- Ward No. 19 	4 	0 	0 	36 	19
Andaman And Nicobar Islands 	South Andamans 	Port Blair 	Port Blair (M CI)- Ward No. 20 	0 	0 	0 	48 	0

Urban Female Population

state_name 	district_name 	town_name 	ward_name 	cattle 	buffalo 	sheep 	goat 	pig
Andaman And Nicobar Islands 	South Andamans 	Port Blair 	Port Blair (M CI)- Ward No. 19 	510 	27 	0 	390 	35
Andaman And Nicobar Islands 	South Andamans 	Port Blair 	Port Blair (M CI)- Ward No. 20 	382 	30 	0 	367 	0

The data doesn't contain village census ids, so we would need to do a fuzzy matching to map all of it to our dataset.

Here is a link of our village level shape files, which have village census2011 id and lgd_village keys, to which we need to map each state-district-subdistrict-village key pair with fuzzy search help.

Once you have matched ids, please share match% across different levels or states and a basic analysis. For this step, please ignore shapes, as we first need to confirm the key matching, so you can just read selective columns without loading whole state shape files which are large for some states.

In case of any doubts or issue resolution, please post your queries here.

Upon validating the matches and some test results, we can finish this first step of pipeline, and move on to the outline proposed by you.

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next steps...

• Join the dataset with village boundary shapefiles to create an enriched vector map.
• Standardize the data (CRS: EPSG:4326) and ensure consistent attribute schema.