Predicting Livelihood Indicators from Community-Generated Street-Level Imagery
Authors: Jihyeon Lee, Dylan Grosz, Burak Uzkent, Sicheng Zeng, Marshall Burke, David Lobell, Stefano Ermon268-276
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our approach in accurately predicting indicators of poverty, population, and health and its scalability by testing in two different countries, India and Kenya. Our code is available at https://github.com/sustainlab-group/mapillarygcn. |
| Researcher Affiliation | Academia | Jihyeon Lee, 1 Dylan Grosz, 1 Burak Uzkent, 1 Sicheng Zeng, 1 Marshall Burke, 2 David Lobell, 2 Stefano Ermon 1 1Department of Computer Science, Stanford University 2Department of Earth Science, Stanford University {jihyeon,dgrosz,buzkent}@cs.stanford.edu |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm', nor any structured code blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/sustainlab-group/mapillarygcn. |
| Open Datasets | Yes | We obtained wealth index values from the most recently completed surveys of the Demographic and Health Survey (DHS), 2015-16 for India and 2014 for Kenya. DHS data is clustered; households within a 5km-radius contribute datapoints individually but share the same geographic coordinates to preserve privacy. |
| Dataset Splits | Yes | For each country, we randomly sample 80% of the clusters as the training set and the remainder is the validation set. |
| Hardware Specification | Yes | We train with batch size 128 and learning rate 0.001 (after trying 0.1, 0.01, and 0.0001) for 50 epochs on a NVIDIA 1080TI GPU with 40G of memory. |
| Software Dependencies | No | The paper mentions various models (e.g., ResNet34, Mask-RCNN) and an optimizer (Adam), but it does not specify any software dependencies with version numbers (e.g., 'PyTorch 1.9', 'TensorFlow 2.0', 'scikit-learn 0.24'). |
| Experiment Setup | Yes | We train with batch size 128 and learning rate 0.001 (after trying 0.1, 0.01, and 0.0001) for 50 epochs on a NVIDIA 1080TI GPU with 40G of memory. We use Adam optimizer (Kingma and Ba 2014) for all the experiments in this study. |