🌍 Model Card for City Identification using DL-SLICER-models
This model card describes the DL-SLICER deep learning tool, which is designed for satellite-based city identification and feature analysis.
Figure 1: Site images from satellite data, representing cities by their International Air Transport Association (IATA) codes, used for city identification model training.
It has been generated using DL-SLICER dataset.
Model Details
Model Description
The models are based on the ResNet architecture and are trained on satellite imagery to classify and distinguish between 45 cities worldwide. The tool utilizes an Explainable AI method, specifically Relevance Class Activation Maps (CAMs), to identify the visual features that characterize each city. The DL-SLICER repository contains four ResNet models (resnet-18, resnet-34, resnet-50, and resnet-101), each with two sets of checkpoints: best.pth (representing the best-performing model) and last.pth (the final model checkpoint).
- Developed by: Ulzhan Bissarinova, Aidana Tleuken, Sofiya Alimukhambetova, Huseyin Atakan Varol, Ferhat Karaca
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Model Sources [optional]
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- Paper: Bissarinova, Ulzhan, Aidana Tleuken, Sofiya Alimukhambetova, Huseyin Atakan Varol, and Ferhat Karaca. 2024. "DL-SLICER: Deep Learning for Satellite-Based Identification of Cities with Enhanced Resemblance" Buildings 14, no. 2: 551. https://doi.org/10.3390/buildings14020551
Uses
The DL-SLICER models are intended for researchers, policymakers, city managers, and urban planners. The models can be used to:
Identify similar cities based on satellite data patterns.
Analyze salient urban features for urban planning, crisis management, and economic policy decisions.
Provide data for indices and concepts related to sustainability and smart cities.
Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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