UHI use case: Krakow dataset implementation

configs/data/heat_krakow.yaml

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+_target_: src.data.base_datamodule.BaseDataModule
+
+dataset:
+  _target_: src.data.heat_krakow_dataset.HeatKrakowDataset
+  data_dir: ${paths.data_dir}
+  modalities:
+    coords: {}
+  use_target_data: true
+  use_features: true
+  use_aux_data: none
+  seed: ${seed}
+  cache_dir: ${paths.cache_dir}
+
+batch_size: 64
+num_workers: 8
+pin_memory: true
+
+split_mode: "from_file"
+save_split: false
+saved_split_file_name: "heat_krakow_split_20260522_1120.pth"
+
+train_val_test_split: [0.7, 0.15, 0.15]
+seed: ${seed}

configs/experiment/heat_krakow_full_fusion_avg.yaml

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+# @package _global_
+# Guatemala performance: R2=0.672, RMSE=1.104, MAE=0.896
+# Kraków performance: R2=0.935, RMSE=0.523, MAE = 0.397
+defaults:
+  - override /model: heat_krakow_full_fusion_avg
+  - override /data: heat_krakow
+  - override /metrics: krakow_regression
+tags: ["heat_island", "krakow", "full_fusion_avg", "best", "regression"]
+seed: 12345
+trainer:
+  min_epochs: 1
+  max_epochs: 100
+data:
+  batch_size: 64
+  dataset:
+    modalities:
+      coords: {}
+      tessera:
+        year: 2024
+        size: 10 #9 resulted in slightly worse performance
+        format: npy
+callbacks:
+  model_checkpoint:
+    monitor: val_r2
+    mode: max
+  early_stopping:
+    monitor: val_r2
+    mode: max
+    patience: 20
+logger:
+  wandb:
+    tags: ${tags}
+    group: "heat_island"
+  aim:
+    experiment: "heat_island"

configs/experiment/heat_krakow_fusion.yaml

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+# @package _global_
+# Variant C: GeoClip + tabular fusion
+# Guatemala performance: R2=0.555, RMSE=1.285, MAE=1.039
+# Kraków performance: R2=0.804, RMSE=0.902, MAE=0.688
+defaults:
+  - override /model: heat_fusion_reg
+  - override /data: heat_krakow
+  - override /metrics: krakow_regression
+
+tags: ["heat_island", "krakow", "fusion", "regression"]
+seed: 12345
+
+trainer:
+  min_epochs: 1
+  max_epochs: 50
+
+data:
+  batch_size: 64
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "heat_island"
+  aim:
+    experiment: "heat_island"

configs/experiment/heat_krakow_geoclip.yaml

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+# @package _global_
+# Guatemala performance: R2=0.323, RMSE=1.607, MAE=1.344
+# Kraków performance: R2=0.450, RMSE=1.519, MAE=1.183
+defaults:
+  - override /model: heat_geoclip_reg
+  - override /data: heat_krakow
+  - override /metrics: krakow_regression
+tags: ["heat_island", "krakow", "coords", "regression"]
+seed: 12345
+trainer:
+  min_epochs: 1
+  max_epochs: 100
+data:
+  batch_size: 64
+callbacks:
+  model_checkpoint:
+    monitor: val_r2
+    mode: max
+  early_stopping:
+    monitor: val_r2
+    mode: max
+    patience: 20
+logger:
+  wandb:
+    tags: ${tags}
+    group: "heat_island"
+  aim:
+    experiment: "heat_island"

configs/experiment/heat_krakow_tabular.yaml

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+# @package _global_
+# Guatemala performance: R2=0.562, RMSE=1.282, MAE=1.040
+# Kraków performance: R2=0.621, RMSE=1.257, MAE=0.949
+defaults:
+  - override /model: heat_tabular_reg
+  - override /data: heat_krakow
+  - override /metrics: krakow_regression
+tags: ["heat_island", "krakow", "tabular", "regression"]
+seed: 12345
+trainer:
+  min_epochs: 1
+  max_epochs: 100
+data:
+  batch_size: 64
+callbacks:
+  model_checkpoint:
+    monitor: val_r2
+    mode: max
+  early_stopping:
+    monitor: val_r2
+    mode: max
+    patience: 20
+logger:
+  wandb:
+    tags: ${tags}
+    group: "heat_island"
+  aim:
+    experiment: "heat_island"

configs/experiment/heat_krakow_tessera_avg.yaml

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+# @package _global_
+# Guatemala performance: R2=0.733, RMSE=1.011, MAE=0.814
+# Kraków performance: R2=0.924, RMSE=0.568, MAE=0.441
+defaults:
+  - override /model: heat_krakow_tessera_avg
+  - override /data: heat_krakow
+  - override /metrics: krakow_regression
+tags: ["heat_island", "krakow", "tessera_avg", "best", "regression"]
+seed: 12345
+trainer:
+  min_epochs: 1
+  max_epochs: 100
+data:
+  batch_size: 64
+  dataset:
+    modalities:
+      coords: {}
+      tessera:
+        year: 2024
+        size: 10
+        format: npy
+callbacks:
+  model_checkpoint:
+    monitor: val_r2
+    mode: max
+  early_stopping:
+    monitor: val_r2
+    mode: max
+    patience: 20
+logger:
+  wandb:
+    tags: ${tags}
+    group: "heat_island"
+  aim:
+    experiment: "heat_island"

configs/metrics/krakow_regression.yaml

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+_target_: src.models.components.metrics.metrics_wrapper.MetricsWrapper
+
+metrics:
+  - _target_: src.models.components.loss_fns.mse_loss.MSELoss
+  - _target_: src.models.components.loss_fns.rmse_loss.RMSELoss
+  - _target_: src.models.components.loss_fns.mae_loss.MAELoss
+  - _target_: src.models.components.metrics.r2.RSquared

configs/model/heat_krakow_full_fusion_avg.yaml

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+_target_: src.models.predictive_model.PredictiveModel
+geo_encoder:
+  _target_: src.models.components.geo_encoders.encoder_wrapper.EncoderWrapper
+  encoder_branches:
+    - encoder:
+        _target_: src.models.components.geo_encoders.geoclip.GeoClipCoordinateEncoder
+    - encoder:
+        _target_: src.models.components.geo_encoders.tabular_encoder.TabularEncoder
+        output_dim: 64
+        geo_data_name: tabular
+    - encoder:
+        _target_: src.models.components.geo_encoders.average_encoder.AverageEncoder
+        geo_data_name: tessera
+  fusion_strategy: concat
+prediction_head:
+  _target_: src.models.components.pred_heads.mlp_regression_head.MLPRegressionPredictionHead
+  nn_layers: 3
+  hidden_dim: 512
+trainable_modules: [geo_encoder.encoder_branches.1, prediction_head]
+normalize_features: false
+metrics: ${metrics}
+optimizer:
+  _target_: torch.optim.Adam
+  _partial_: true
+  lr: 0.0001
+  weight_decay: 0.0
+scheduler:
+  _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
+  _partial_: true
+  mode: min
+  factor: 0.1
+  patience: 10
+loss_fn:
+  _target_: torch.nn.MSELoss

configs/model/heat_krakow_tessera_avg.yaml

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+_target_: src.models.predictive_model.PredictiveModel
+geo_encoder:
+  _target_: src.models.components.geo_encoders.average_encoder.AverageEncoder
+  geo_data_name: tessera
+prediction_head:
+  _target_: src.models.components.pred_heads.mlp_regression_head.MLPRegressionPredictionHead
+  nn_layers: 2
+  hidden_dim: 512
+trainable_modules: [prediction_head]
+normalize_features: true
+metrics: ${metrics}
+optimizer:
+  _target_: torch.optim.Adam
+  _partial_: true
+  lr: 0.001
+  weight_decay: 0.0
+scheduler:
+  _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
+  _partial_: true
+  mode: min
+  factor: 0.1
+  patience: 10
+loss_fn:
+  _target_: torch.nn.MSELoss