Datasets:
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class FilePaths:
results_path = "results/"
saved_models_path = "saved_model_files/"
object_dict_path = "data/object_dicts/"
dataset_dict_path = "data/dataset_dicts/"
property_dict_path = "data/property_dicts/"
dataset_partition_path = "data/dataset_partitions/"
class Constants:
dataset_name = "Hague" # "Hague", "delivery3", "bo_em", "gpkg"
synthetic_folder_name = "example" # Relevant only if dataset_name is "synthetic"
evaluation_mode = "matching" # "blocking", "matching"
dataset_size_version = 'medium' # 'small', 'medium', 'large'
matching_cands_generation = 'negative_sampling' # 'negative_sampling', 'blocking-based'
neg_samples_num = 2 # 2, 5
seeds_num = 1
train_ratio = 0.6
val_ratio = 0.2
test_ratio = 1 - train_ratio - val_ratio
max_ratio_val = 1000 # Avoid infinity values
load_object_dict = False # Must be False when disaster simulation is active
save_object_dict = True # Cache object dict to avoid reloading from disk
load_train_items = False # Load existing preparatory items
save_property_dict = True # Cache property dict to avoid recomputing
load_property_dict = False # Load the properties dictionary
save_dataset_dict = True # Cache dataset dict to avoid recomputing
load_dataset_dict = False # load existing dataset dictionary
file_name_suffix = 'allmodels_v1' # Stable suffix so model files are reusable across runs
max_grid_cells = 20 # None = all cells; 50 = medium run on 8GB RAM
class TrainingPhase:
training_ratio = 0.5 # Number of positive samples
neg_pairs_ratio = 4 # Number of negative samples per positive sample
run_preparatory_phase = True # If False, the preparatory phase will not be run
class Features:
knn_buildings = 0 # Number of nearest buildings to consider
knn_roads = 0 # Number of nearest roads to consider
operator = 'division' # 'division', 'concatenation'
object_properties = ["bounding_box_width", "bounding_box_length", "area", "perimeter", "perimeter_ind",
"volume", "convex_hull_area", "convex_hull_volume", "ave_centroid_distance", "height_diff",
"num_floors", "axes_symmetry", "compactness_2d", "compactness_3d", "density",
"elongation", "shape_ind", "hemisphericality", "fractality", "cubeness", "circumference",
"aligned_bounding_box_width", "aligned_bounding_box_length", "aligned_bounding_box_height",
"num_vertices"]
# object_properties = ["circumference", "density", "convex_hull_area"]
normalization = 'log_transform' # 'log_transform', None
neighborhood = []
roads = []
class Blocking:
blocking_method = 'bkafi' # 'bkafi', 'bkafi_without_SDR', 'ViT-B_32', 'ViT-L_14', 'centroid'
# 'coordinates', 'coordinates_transformed'
cand_pairs_per_item_list = [i for i in range(1, 21)] # total number of neighbors per each candidate object
nn_param = cand_pairs_per_item_list[-1] + 1 # number of nearest neighbors to retrieve as candidates
nbits = 10 # number of bits to use for LSH
# bkafi_dim_list = [dim for dim in range(1, len(Features.object_properties))] # Number of important features to
# use for blocking (for the bkafi method)
bkafi_dim_list = [dim for dim in range(1, len(Features.object_properties))] # Number of important features to use
dist_threshold = None # Define it as a hyperparameter or in a flexible manner
sdr_factor = False # If True, the SDR factor will be used in the blocking method
bkafi_criterion = 'feature_importance' # 'std', 'feature_importance'
# Neighborhood-aware negative sampling
neighborhood_radius = 500.0 # meters (EPSG:7415) — radius for spatial negative sampling
neighborhood_neg_ratio = 0.7 # fraction of negatives drawn from within-radius neighbors vs. random
class DataPartition:
grid_cell_size = 500.0 # meters (EPSG:7415) — side length of each spatial grid cell
train_ratio = 0.6 # fraction of grid cells assigned to training
contiguous_test = True # If True, test cells form a spatially contiguous region (BFS from
# a corner) so the demo app covers a coherent train-only area.
class DisasterSimulation:
enabled = True
# CRS simulation — random rotation + large translation applied globally to all cands
crs_simulation = True # simulate unknown CRS (no absolute reference)
# Damage simulation — per-building random height reduction
damage_probability = 0.8 # fraction of cand buildings to damage
min_damage_factor = 0.3 # minimum remaining height fraction (0.3 = 70% collapsed)
max_damage_factor = 0.95 # maximum remaining height fraction (near-undamaged)
class Alignment:
enabled = True
min_anchor_pairs = 3 # minimum high-confidence matches required to attempt alignment
confidence_threshold = 0.8 # geometric classifier score threshold for anchor selection
max_residual_threshold = 50.0 # meters — reject alignment if mean anchor error exceeds this
alpha = 0.5 # weight: 1.0 = geometric score only, 0.0 = spatial score only
output_crs = "EPSG:7415" # index dataset CRS — output aligned CityJSON in this CRS
use_ransac = True # use RANSAC to find robust transform instead of plain SVD
ransac_iterations = 1000 # number of RANSAC trials
ransac_inlier_threshold = 10.0 # meters — anchor is inlier if residual < this after applying R, t
spatial_sigma = 3.0 # meters — Gaussian decay length for post-alignment spatial score.
# spatial(d) = exp(-d²/(2·σ²)). σ ≈ median true-match residual;
# σ=3 m gives spatial(0)=1, spatial(3)=0.61, spatial(10)≈0.004.
post_align_knn_cutoff = 7.0 # meters — for --post-align-blocking mode in demo/inference.py.
# After alignment succeeds, replace BKAFI pool with per-cand 1-NN
# against full index; accept iff post-alignment distance ≤ cutoff.
class Models:
load_trained_models = False
cv = 3
model_to_use = 'XGBClassifier' # Used only for predict.py and feature_importances.py
model_list = ['XGBClassifier', # 'GradientBoostingClassifier', 'BaggingClassifier',
'RandomForestClassifier', 'AdaBoostClassifier', 'MLPClassifier']
blocking_model = 'RandomForestClassifier' # Used only for blocking and for advanced evaluation
params_dict = {
'RandomForestClassifier': {"n_estimators": [50],
"max_depth": [5],
"min_samples_split": [2],
"max_features": ["sqrt"]},
'SVC': {'C': [0.1, 0.5],
'kernel': ['rbf'],
'gamma': ['scale'],
'degree': [2]
},
'LogisticRegression': {'solver': ['lbfgs', 'saga'],
'multi_class': ['auto'],
'C': [0.01, 0.1, 1]
},
'MLPClassifier': {'hidden_layer_sizes': [(64, 32)],
'activation': ['relu'],
'solver': ['adam'],
'batch_size': [16],
'max_iter': [500],
'early_stopping': [True],
'n_iter_no_change': [20],
},
'AdaBoostClassifier': {'n_estimators': [100],
'learning_rate': [0.1],
'algorithm': ['SAMME']
},
'GradientBoostingClassifier': {'loss': ['log_loss'],
'learning_rate': [0.1],
'n_estimators': [100],
'max_depth': [3],
'min_samples_split': [3],
'max_features': ['sqrt']
},
'BaggingClassifier': {'n_estimators': [50],
'max_samples': [0.8],
'max_features': [0.8],
'bootstrap': [True]
},
'XGBClassifier': {'max_depth': [4],
'objective': ['binary:logistic'],
'learning_rate': [0.1],
'n_estimators': [100],
'gamma': [0],
'tree_method': ['hist'],
'n_jobs': [4],
}
}
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