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"en": "1. Overview\r\n\r\nThis repository contains FGI-EMIT, a multispectral high-density airborne laser scanning benchmark dataset for individual tree segmentation, presented in the paper Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset (Ruoppa et al., 2026). FGI-EMIT consists of 19 boreal forest plots spanning a wide range of forest types and also includes built environment, extending its applicability to urban forests. Each tree instance across all plots has been manually annotated in 3D, resulting in a total of 1,561 trees with high-quality instance annotations, with a particular focus on small understory trees that are notoriously challenging to segment. All point clouds in the dataset are multispectral and include reflectance information captured simultaneously at three distinct wavelengths.\r\n\r\nThe multispectral point clouds were originally acquired in the Espoonlahti district of Espoo, Finland (60.1462°N, 24.6587°E) on July 20 and 28, 2023, using the Finnish Geospatial Research Institute’s (FGI’s) in-house developed laser scanning system, HeliALS-TW. The system includes three separate RIEGL LiDAR scanners, VUX-1HA (1,550 nm), miniVUX-1DL (905 nm), and VQ-840-G (532 nm), referred to as scanners 1, 2, and 3, respectively. For a detailed description of the FGI-EMIT dataset, including data acquisition, preprocessing, and an extensive benchmark of popular individual tree segmentation (ITS) methods, covering both conventional unsupervised algorithms and deep learning (DL) approaches, please refer to the associated paper.\r\n\r\n2. Data usage\r\n\r\nFGI-EMIT is a machine learning (ML) and deep learning-ready dataset for benchmarking individual tree segmentation methods. This section provides a brief overview of how the data can be used for benchmarking purposes. To ensure comparability between studies, we encourage adhering to the standardized benchmarking procedure presented in Appendix A.4 of the associated paper when evaluating ITS methods using the FGI-EMIT dataset.\r\n\r\n2.1 Data split\r\n\r\nThe 19 plots in the FGI-EMIT dataset have been divided into training and test sets, to be used as follows:\r\n\r\n\r\n\r\nTraining set: intended for method development. This data can be used, for example, to train ML- and DL-based individual tree segmentation models and/or to optimize hyperparameters of unsupervised segmentation algorithms.\r\n\r\nTest set: intended only for evaluating the accuracy of individual tree segmentation methods. To prevent data leakage, the test set must not be used at any stage of model training or hyperparameter optimization.\r\n\r\n\r\n70% of the 19 plots were assigned to the training set, with the remaining 30%, that is, 6 plots, assigned to the test set. Due to substantial variation in plot complexity, the test set was selected using stratified random sampling, in which the plots were divided into three groups based on forest density.\r\n\r\nIn this repository, the FGI-EMIT data are provided pre-divided into training and test directories. The specific data split is also listed in Table 1. While FGI-EMIT does not include a designated validation set, a portion of the training data can be set aside for purposes such as model selection, hyperparameter tuning, and overfitting prevention. The proportion of training data allocated to validation can be determined by the user to suit the particular use case. For consistency, we recommend using the same three-plot validation set as in the benchmark (plots with IDs 1019, 1022, and 1031) in applications where validation is required.\r\n\r\n \r\n\r\nTable 1: Training-test split of the FGI-EMIT dataset.\r\n\r\n\r\n\r\n\r\nSet\r\nPlot IDs\r\nNumber of trees\r\n\r\n\r\nTraining\r\n1001, 1003, 1005, 1009, 1010, 1013, 1019, 1020, 1022, 1023, 1024, 1027, 1031\r\n1098\r\n\r\n\r\nTest\r\n1002, 1004, 1008, 1012, 1018, 1028\r\n463\r\n\r\n\r\n\r\n\r\n2.2 Accuracy computation\r\n\r\nIndividual tree segmentation performance on FGI-EMIT should be assessed using common instance segmentation metrics, specifically Recall, Precision, F1-score, Coverage (Cov), and Average Precision (AP). Additionally, if crown category information is available, category-level recall can be computed to assess the effects of relative crown positioning on method accuracy.\r\n\r\nA Python script for accuracy computation is provided in the accuracy directory of this repository. The script matches predicted instances to ground truth based on intersection over union (IoU) and computes the required metrics, as described in Section 3.4.2 of the associated paper. For consistency, we strongly recommend using the official accuracy computation script when evaluating ITS methods on FGI-EMIT. Detailed documentation on how to configure the script is also provided (DOCS.md). The script supports point clouds in the las, laz and ply file formats. By default, it is configured for the annotation format used in FGI-EMIT, but it can be easily adapted to work with other formats, such as that of the FOR-InstanceV2 dataset. The code is also available in the FGI-EMIT GitHub repository.\r\n\r\n3. Data description\r\n\r\nThe FGI-EMIT dataset contains a total of 19 cylindrical forest plots of varying sizes, representing a wide range of boreal forest environments, including coniferous-dominated, deciduous-dominated, and mixed forests, as well as planted trees. In addition, some plots contain man-made structures, such as buildings and vehicles. The multispectral LiDAR point clouds for the 19 plots are provided in the las file format. Each point cloud includes manually generated point-level instance and semantic ground truth annotations, as well as several non-standard attributes. Furthermore, all instances in the dataset have been assigned a crown category that describes their size and position relative to neighboring trees. For a complete description of the dataset, please refer to Section 3 and Appendix A of the associated paper.\r\n\r\n3.1 Ground truth labels\r\n\r\nFor each of the 19 plots in the FGI-EMIT dataset, individual trees have been manually annotated at the point level. All sections of the point cloud that could be definitively identified as trees and were at least 3 meters in height have been segmented into individual instances. For partial trees along the plot perimeter, only those for which at least approximately 50% of the tree appeared to lie within the plot boundaries were segmented. Points belonging to the same tree have been assigned a common positive integer ID, while non-tree points are assigned the value 0. The tree instance IDs are stored in the field tree_index and are unique at the plot level.\r\n\r\nIn addition to instance annotations, FGI-EMIT includes rudimentary semantic labels for common categories present in the data. In particular, in addition to trees, man-made objects have each been assigned their own semantic classes. Furthermore, partial trees along plot perimeters that were not given instance labels have been assigned to a separate class. The semantic labels are stored in the classification field of the point clouds. The primary purpose of the semantic annotations is to facilitate the filtering of object categories that could hinder the performance of ITS methods, which is particularly relevant for unsupervised approaches. The definitions of each semantic category are listed in Table 2.\r\n\r\nIn the ITS benchmark presented in the associated paper, points in class 5 were always removed from the data. For unsupervised algorithms, which are not inherently designed to handle data from non-forest environments, points in classes 2–4 were also removed. In contrast, since DL models can learn to classify built environments as background, points in classes 2–4 were remapped to class 0.\r\n\r\n \r\n\r\nTable 2: Descriptions of the semantic categories available in the FGI-EMIT dataset.\r\n\r\n\r\n\r\n\r\nName\r\nID\r\nDescription\r\n\r\n\r\nOther\r\n0\r\nPoints not belonging to any other category. A majority of points in this class are either ground or understory vegetation.\r\n\r\n\r\nTree\r\n1\r\nPoints that are part of any tree instance.\r\n\r\n\r\nBuilding\r\n2\r\nBuildings and similar smaller man-made structures, such as sheds. This also includes structures attached to buildings, e.g. fences.\r\n\r\n\r\nVehicle\r\n3\r\nMotorized vehicles, such as cars, trucks and buses.\r\n\r\n\r\nPole\r\n4\r\nVertical pole-like structures, such as lamp-posts, traffic signs and utility poles.\r\n\r\n\r\nOut\r\n5\r\nPoints belonging to trees that reside mostly outside the plot boundaries and were therefore not assigned to any tree instance.\r\n\r\n\r\n\r\n\r\n3.2 Tree locations, heights, and crown categories\r\n\r\nAn $xy$-location and height were computed for each tree instance based on the point cloud data. The tree location was determined by computing the centroid of a convex hull fitted to the $xy$-plane projection of the crown points. The tree height was then computed as follows:\r\n\r\n$h_{\\text{tree}}=z_{\\max,\\text{tree}}-\\min\\left\\{z_{\\min,\\text{ground}},z_{\\min,\\text{tree}}\\right\\},$\r\n\r\nwhere $z_{\\max,\\text{tree}}$ is the maximum $z$-coordinate in the tree instance, $z_{\\min,\\text{ground}}$ is the minimum $z$-coordinate of the ground directly below the instance, and $z_{\\min,\\text{tree}}$ is the minimum $z$-coordinate within the instance.\r\n\r\nTo facilitate the analysis of how relative crown positioning affects the accuracy of individual tree segmentation, all tree instances were classified into four distinct crown categories based on height differences and distances relative to neighboring trees. The crown category definitions and corresponding tree counts in the FGI-EMIT dataset are listed in Table 3. The categories were determined using an automatic algorithm based on the tree locations and heights computed from the instance point clouds.\r\n\r\nTree locations, heights, and crown categories for each plot, along with plot-level tree counts and densities, are provided in a machine-readable format in the file plot_data.yaml. The information in the file is grouped by plot ID and tree ID. The file format is compatible with the accuracy computation script, enabling crown-category-level recall to be computed.\r\n\r\n \r\n\r\nTable 3: Specifications of the four tree crown categories. Two trees were considered neighbors if the distance between them was less than 3 m.\r\n\r\n\r\n\r\n\r\nCategory\r\nNumber of trees\r\nDescription\r\nDefinition\r\n\r\n\r\nA\r\n611 (39%)\r\nIsolated or dominant trees\r\nTree has no neighboring trees or is ≥2 m higher than all neighbors.\r\n\r\n\r\nB\r\n308 (20%)\r\nGroup of similar trees\r\nTree has at least one neighboring tree and is <2 m higher than all neighbors.\r\n\r\n\r\nC\r\n451 (29%)\r\nTree alongside a dominant tree\r\nTree has at least one neighboring tree which is ≥2 m higher and at a distance of ≥1.5 m.\r\n\r\n\r\nD\r\n191 (12%)\r\nTree under a dominant tree\r\nTree has at least one neighboring tree which is ≥ 2 m higher and at a distance of <1.5 m.\r\n\r\n\r\n\r\n\r\n3.3 Additional point cloud attributes\r\n\r\nAll point clouds in the FGI-EMIT dataset include the non-standard attributes listed in Table 4. To facilitate, for example, the automatic filtering of points with missing attribute values, the same information is also provided in a machine-readable format in the file metadata.yaml.\r\n\r\n \r\n\r\nTable 4: Additional attributes available in the FGI-EMIT point clouds. The missing value column indicates the placeholder used for attributes when no corresponding record exists for a point.\r\n\r\n\r\n\r\n\r\nAttribute\r\nMissing value\r\nDescription\r\n\r\n\r\nred\r\n-\r\nScaled reflectance from scanner 1.\r\n\r\n\r\ngreen\r\n-\r\nScaled reflectance from scanner 2.\r\n\r\n\r\nblue\r\n-\r\nScaled reflectance from scanner 3.\r\n\r\n\r\nintensity_1\r\n0\r\nIntensity from scanner 1.\r\n\r\n\r\nintensity_2\r\n0\r\nIntensity from scanner 2.\r\n\r\n\r\nintensity_3\r\n0\r\nIntensity from scanner 3.\r\n\r\n\r\namplitude_1\r\n0\r\nAmplitude from scanner 1.\r\n\r\n\r\namplitude_2\r\n0\r\nAmplitude from scanner 2.\r\n\r\n\r\namplitude_3\r\n0\r\nAmplitude from scanner 3.\r\n\r\n\r\nreflectance_1\r\n-9,999\r\nReflectance from scanner 1.\r\n\r\n\r\nreflectance_2\r\n-9,999\r\nReflectance from scanner 2.\r\n\r\n\r\nreflectance_3\r\n-9,999\r\nReflectance from scanner 3.\r\n\r\n\r\ndeviation_1\r\n65,535\r\nEcho deviation from scanner 1.\r\n\r\n\r\ndeviation_2\r\n65,535\r\nEcho deviation from scanner 2.\r\n\r\n\r\ndeviation_3\r\n65,535\r\nEcho deviation from scanner 3.\r\n\r\n\r\nuser_data\r\n-\r\nThis value indicates which scanner an individual point is originally from. The points have been labeled either 1, 2, or 3 for scanners 1, 2, and 3 respectively.\r\n\r\n\r\ntree_index\r\n-\r\nManually generated tree instance annotation. All points in a tree instance share the same value, and each tree instance within a plot has a unique index. Points not belonging to any tree instance have tree_index set to 0.\r\n\r\n\r\nedge\r\n-\r\nTree instances on the edge of the plot with some parts missing have this value set to 1 for all points. All other points have this value set to 0.\r\n\r\n\r\ndead\r\n-\r\nTree instances identified as dead via visual inspection have this value set to 1 for all points. All other points have this value set to 0.\r\n\r\n\r\n\r\n\r\n4. Citation\r\n\r\nAny scientific publication using the data should cite the following paper:\r\n\r\nRuoppa, L., Hietala, T., Seppänen, V., Taher, J., Hakala, T., Yu, X., Kukko, A., Kaartinen, H., and Hyyppä, J., 2026. Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset. ISPRS J. Photogramm. Remote Sens., 236:569–605, 2026. doi:10.1016/j.isprsjprs.2026.04.021.\r\n\r\nBibTeX:\r\n\r\n@article{ruoppa2026benchmarking, title = {{Benchmarking individual tree segmentation using multispectral airborne laser scanning data: The FGI-EMIT dataset}}, author = {Lassi Ruoppa and Tarmo Hietala and Verneri Seppänen and Josef Taher and Teemu Hakala and Xiaowei Yu and Antero Kukko and Harri Kaartinen and Juha Hyyppä}, journal = {ISPRS J. Photogramm. Remote Sens.}, volume = {236}, pages = {569--605}, year = {2026}, doi = {10.1016/j.isprsjprs.2026.04.021},}"
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"en": "================================================================================STEM FOCAL-SERIES DEFECT SEGMENTATION — WORKFLOW README================================================================================\r\n\r\nThis project contains a complete, reproducible workflow for training a U-Netensemble to segment defects in focal-series STEM (HAADF) images, and forevaluating the trained model on a completely held-out test set.\r\n\r\nThe workflow is driven by a single Jupyter notebook:\r\n\r\n tutorial_remade.ipynb\r\n\r\nwith helper functions in:\r\n\r\n aux_functions/stem.py\r\n\r\n================================================================================1. WHAT THIS PROJECT DOES================================================================================\r\n\r\nGiven a set of STEM focal-series images (a stack of images at differentdefocus values), the workflow:\r\n\r\n 1. Builds an HDF5 training dataset from pre-simulated PKL files. 2. Trains a first U-Net ensemble (v1) on this dataset. 3. Uses ensemble disagreement (predictive entropy) to identify the most uncertain training samples. 4. Retrains an augmented ensemble (v2) that upweights those uncertain samples. 5. Runs inference with the final ensemble (v2) on unseen held-out data. 6. Converts predicted and ground-truth masks into atomistic XYZ files, clusters them into defect clusters, fits PCA ellipsoids, and computes voxel-level metrics (precision, recall, IoU, F1, Dice).\r\n\r\nThe pipeline is designed so that the held-out test set is never usedfor dataset creation or training.\r\n\r\n================================================================================2. EXPECTED DIRECTORY STRUCTURE================================================================================\r\n\r\nRun the notebook from the project root. The project must contain:\r\n\r\n project/ ├── tutorial_remade.ipynb ├── requirements.txt ├── README.txt (this file) ├── pkls_training/ │ └── ... training PKL files ... ├── testing_set_heldout/ │ └── ... held-out PKL files ... └── aux_functions/ └── stem.py\r\n\r\nNotes:\r\n\r\n * pkls_training/ — input PKLs used to CREATE the training dataset. * testing_set_heldout/— unseen PKLs used ONLY for final evaluation. Never move these into pkls_training/. * aux_functions/stem.py must be present; the notebook imports many helper functions from it (tile_to_201, build_label_mask_from_positions, find_columns_and_defects, ...).\r\n\r\nThe notebook will raise FileNotFoundError if any of these are missing.\r\n\r\n================================================================================3. WHAT THE PKL FILES MUST CONTAIN================================================================================\r\n\r\nEach training PKL is expected to be a Python list (or tuple) of per-slicedictionaries, one per defocus value. Every per-slice dict must contain:\r\n\r\n \"image\" : 2D numpy array (the STEM image at this defocus) \"sampling\" : float (Å / pixel) \"defocus\" : float (Å) \"positions\" : (N, 3) numpy array of atom positions (Å) \"extent\" : (Lx, Ly) in Å\r\n\r\nOptionally:\r\n\r\n \"num_atoms\", \"time\", \"energy\", \"method\", \"Cs\", ... (these are ignored by the dataset builder but are preserved elsewhere)\r\n\r\nThe held-out PKLs use the same structure but must be located inside adirectory tree of the form:\r\n\r\n testing_set_heldout/ └── / ├── patch01_stem/results.pkl ├── patch02_stem/results.pkl ├── ... └── patch16_stem/results.pkl\r\n\r\nThe notebook automatically discovers any directory containing\"patch16_stem/results.pkl\".\r\n\r\n================================================================================4. INSTALLATION================================================================================\r\n\r\nSee requirements.txt for the full package list. A typical CUDA 12.1 setup:\r\n\r\n # 1) core scientific stack pip install \"numpy>=1.24,<2.0\" scipy scikit-learn h5py matplotlib\r\n\r\n # 2) PyTorch (CUDA 12.1 build) pip install torch --index-url https://download.pytorch.org/whl/cu121\r\n\r\n # 3) STEM simulation + atomistic handling pip install abtem ase\r\n\r\n # 4) GPU memory helpers used by aux_functions/stem.py pip install cupy-cuda12x\r\n\r\n # 5) Jupyter pip install jupyterlab ipykernel notebook\r\n\r\n # 6) Register the kernel used by the notebook python -m ipykernel install --user --name torch-cu121 \\ --display-name \"Python (torch-cu121)\"\r\n\r\nIMPORTANT (CPU-only users): aux_functions/stem.py imports cupy at module level, so it will fail to import without a CUDA-capable GPU and cupy installed. If you only run the training / evaluation path of the notebook, you can safely wrap the cupy import in stem.py with try/except and set cp = None. Only the simulate_stem_images_defocus() function actually uses it.\r\n\r\n================================================================================5. HOW TO RUN================================================================================\r\n\r\n--------------------------------------------------------------------------------5.1 Quick start (all-in-one)--------------------------------------------------------------------------------\r\n\r\n cd /path/to/project jupyter lab tutorial_remade.ipynb\r\n\r\nThen run the cells in order:\r\n\r\n Cell 1 — configuration (STEP 1) Cell 2 — dataset + training definitions (STEP 2) Cell 3 — execute dataset creation / training (STEP 4) Cell 4 — held-out testing configuration (STEP 5) Cell 5 — held-out evaluation implementation (STEP 6) Cell 6 — execute held-out evaluation (STEP 8)\r\n\r\n--------------------------------------------------------------------------------5.2 Recommended staged approach--------------------------------------------------------------------------------\r\n\r\nThe notebook is designed to be run in three stages, controlled by the threetop-level flags in the first configuration cell:\r\n\r\n MAKE_DATASET = True # create dataset_training.h5 from pkls_training/ RUN_TRAINING = True # train ensemble v1 and v2 RUN_TESTING = False # run held-out evaluation\r\n\r\nSTAGE 1 — Build the dataset only MAKE_DATASET = True RUN_TRAINING = False RUN_TESTING = False → produces dataset_training.h5 in the project root.\r\n\r\nSTAGE 2 — Train the ensembles MAKE_DATASET = False # dataset already exists RUN_TRAINING = True RUN_TESTING = False → produces: checkpoints_ensemble/ensemble_sigmoid_v1/ checkpoints_ensemble/ensemble_sigmoid_v2/\r\n\r\nSTAGE 3 — Held-out evaluation MAKE_DATASET = False RUN_TRAINING = False RUN_TESTING = True → produces one log file per threshold, e.g.: results_checkpoints_ensemble_heldout_TH0p6.txt plus gt_atoms.xyz, pred_atoms.xyz, gt_atoms_cl0.xyz, pred_atoms_cl0.xyz inside each patch directory.\r\n\r\n--------------------------------------------------------------------------------5.3 Running from the project root (required)--------------------------------------------------------------------------------\r\n\r\n PROJECT_ROOT = Path.cwd()\r\n\r\nThe notebook assumes you launched Jupyter (or a script) from the projectroot — the directory that contains pkls_training/, testing_set_heldout/,and aux_functions/.\r\n\r\n================================================================================6. KEY CONFIGURATION PARAMETERS================================================================================\r\n\r\nAll parameters live in STEP 1 (first code cell of the notebook).\r\n\r\n--------------------------------------------------------------------------------6.1 Dataset creation--------------------------------------------------------------------------------\r\n\r\n DOSE_PER_AREA = 1e6 # Poisson noise dose for training images TILE_TO_201 = True # tile images to 201 x 201 DEFOCUS_MARGIN = 5 # ± Å around slice defocus for mask building WINDOW = 19 # number of defocus slices per training sample\r\n\r\n SIGMA_MIN, SIGMA_MAX, SIGMA_STEPS = 0.01, 0.1, 25 # Gaussian blur range (Å) used during training INTERPOLATE_STEP = 0.1 # abtem interpolation step H5_CHUNK = (16, WINDOW, 201, 201)\r\n\r\nA training sample is a stack of WINDOW consecutive defocus slices from asingle PKL. Overlapping windows are extracted with stride 1, giving roughly(N_slices_in_pkl − WINDOW + 1) samples per PKL.\r\n\r\n--------------------------------------------------------------------------------6.2 Ensemble / training--------------------------------------------------------------------------------\r\n\r\n SEEDS_V1 = [17, 29, 43] # 3 initial ensemble members SEEDS_V2 = [201, 301, 401] # 3 augmented ensemble members\r\n\r\n BASE_FILTERS = 64 POS_WEIGHT = 320.0 # BCEWithLogitsLoss pos_weight EPOCHS_V1 = 200 EPOCHS_V2 = 500 LR = 1e-3 BATCH_SIZE = 8 BAG_FRACTION = 0.8 # fraction of training indices per bag\r\n\r\nTraining uses Adam + AMP (autocast/GradScaler) on CUDA when available.\r\n\r\n--------------------------------------------------------------------------------6.3 Active-learning selection--------------------------------------------------------------------------------\r\n\r\n TOP_K = 50 # number of most-uncertain training samples to upweight TOP_Q = 0.95 # top quantile of per-pixel entropy to average\r\n\r\nUncertainty is the mean per-pixel Bernoulli entropy of the ensemble meanprobability, averaged over the top-q pixels of each slice and then overslices. The top-K highest-uncertainty samples are upweighted (viaWeightedRandomSampler) when training v2.\r\n\r\n--------------------------------------------------------------------------------6.4 Held-out evaluation--------------------------------------------------------------------------------\r\n\r\n THRESH = 0.6 THRESHOLDS = [0.4, 0.5, 0.6, 0.7] # thresholds to sweep over\r\n\r\n DEFOCUS_MARGIN = 5.0 # ± Å around slice defocus when building GT masks RADIUS = 1.0 # Å, neighborhood radius for column detection APPLY_NOISE = True GAUSS_SIGMA = 0.05 DO_NORMALIZE = True FORCE_HW = (201, 201)\r\n\r\n PATCH_GRID = (4, 4) # 16 patches arranged 4 x 4 Lx, Ly, Lz = 80.0, 80.0, 190.0 # Å, physical sample volume VOXEL_SIZE = (2.0, 2.0, 10.0) # Å / voxel\r\n\r\n a_Si = 5.431 CLUSTER_CUTOFF = 2 * a_Si # Å, defect-cluster linkage distance USE_PBC = False NSIGMAS = 2.0 # semi-axes = NSIGMAS * sqrt(eigval)\r\n\r\n================================================================================7. OUTPUTS================================================================================\r\n\r\nAfter a successful run you will find:\r\n\r\n project root ├── dataset_training.h5 ├── checkpoints_ensemble/ │ ├── ensemble_sigmoid_v1/ │ │ ├── index.json │ │ ├── member_00.pt │ │ ├── member_01.pt │ │ └── member_02.pt │ └── ensemble_sigmoid_v2/ │ ├── index.json │ ├── member_00.pt │ ├── member_01.pt │ └── member_02.pt └── results_checkpoints_ensemble_heldout_TH*.txt\r\n\r\nInside every testing_set_heldout//patch**XX**_stem/ directory youwill also find:\r\n\r\n results_masks.pkl — gt_masks, pred_masks, prob, defocus, positions, extent, sampling gt_atoms.xyz — all GT voxels turned into atoms pred_atoms.xyz — all predicted voxels turned into atoms gt_atoms_cl0.xyz — largest GT cluster pred_atoms_cl0.xyz — largest predicted cluster\r\n\r\n================================================================================8. TROUBLESHOOTING================================================================================\r\n\r\n1) \"Unreadable Notebook: ... TypeError('sequence item 0: expected str ...')\" The .ipynb JSON is malformed — one cell has a nested list inside \"source\". Fix by flattening the offending cell's source, or re-open in a JSON-aware editor.\r\n\r\n2) \"abtem is required to MAKE_DATASET but could not be imported\" Install abtem: pip install abtem (abtem pulls in dask and numba automatically.)\r\n\r\n3) \"This script requires 'h5py'\" Install h5py: pip install h5py\r\n\r\n4) ImportError on cupy when doing \"from stem import ...\" stem.py imports cupy at module level. Either install a matching cupy-cudaXXx package, or edit stem.py to make the cupy import optional (it is only used inside simulate_stem_images_defocus).\r\n\r\n5) \"Missing training directory: .../pkls_training\" Ensure you run the notebook from the project root and that pkls_training/ exists with at least one .pkl file.\r\n\r\n6) \"No held-out patch directories were found\" Ensure testing_set_heldout/ contains at least one subdirectory with patch16_stem/results.pkl. Note that the discovery pattern is patch16_stem (16, not 01) by design — every run directory must contain a patch16_stem/results.pkl.\r\n\r\n7) Out of GPU memory during training Reduce BATCH_SIZE, BASE_FILTERS, or WINDOW in STEP 1.\r\n\r\n8) \"Missing trained ensemble: .../checkpoints_ensemble/ensemble_sigmoid_v2\" Either run the training stage first, or edit CHECKPOINT_DIR in STEP 5 to point to wherever your trained ensemble lives.\r\n\r\n================================================================================10. FILE INVENTORY================================================================================\r\n\r\n tutorial_remade.ipynb Main workflow notebook (dataset, training, evaluation).\r\n\r\n aux_functions/stem.py STEM simulation helpers, defect detection, mask construction, XYZ tiling, column finding, tilt routines, LAMMPS/XYZ converters.\r\n\r\n requirements.txt Python package list.\r\n\r\n README.txt This document.\r\n\r\n================================================================================END OF README================================================================================"
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All quantities are derived from the publicly available Heat Pump KEYMARK certification database (https://keymark.eu/, accessed 18 March 2026 (population models and training data are frozen to that snapshot; a separate temporal hold-out uses a later snapshot retrieved 20 August 2026, from which only derived cohort and error data are included, without refitting)). The external laboratory hold-out (part of v2) additionally uses measured values transcribed from the publicly published test-result list of the WPZ Buchs heat-pump test centre (OST; edition 29 May 2026, see scop_validation/SOURCES_wpz.md); only the transcribed factual measurements and the derived errors are deposited, not the source document. The deposit is not a verbatim copy of that registry: it contains the authors’ deduplicated, harmonised, quality-screened, and computed compilation in the authors’ own structure. 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