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EcoSIS Urban Materials Spectral Library reflectance

ecosis · NIR

EcoSIS Urban Materials Spectral Library reflectance. v2.0 standardized NIRS package: 1 spectral source(s), 1 declared target(s). Auto-generated from dataset_card.json (verify before publication).

nirv2ecosis
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Private dataset. Full metadata and metrics are shown, but the bytes are not redistributed here — exporting the data requires a Dataverse token. The identity card carries no spectra, only descriptive statistics.
60
samples
256
wavelengths
1
sources
1
targets
14
metadata
NIR
family

Dataset property explorer

Mean profile risk0.60
Highest axisArtefacts locaux · 1.00
Diagnostics8
Sources profiled1
EcoSIS Urban Materials Spectral Library reflectance property profile0.250.50.751integritynoiseartefactsbaselinePCA outliersreferencerepeatabilitystructureEcoSIS Urban Materials Spectral Library reflectance profileintegrity: 0.00noise: 0.00artefacts: 1.00baseline: 1.00PCA outliers: 0.82reference: 1.00repeatability: 0.00structure: 1.00EcoSIS Urban Ma…0 center · 1 outer ring · outward = stronger anomaly / heterogeneity signal

Profile axes

Intégrité0.00
Artefacts locaux1.00
Bruit0.00
Outliers PCA0.82
Distance à la référence1.00
Répétabilité0.00
Baseline / forme1.00
Structure multi-régimes1.00
Diagnostic hypotheses00.250.50.751hypothesis scoreSplice / raccord détecteursSplice / raccord détecteurs: 0.900.90Erreur calibration / référenc…Erreur calibration / référence blanche: 0.810.81Fond différentFond différent: 0.750.75Erreur interpolation / réécha…Erreur interpolation / rééchantillonnage: 0.730.73Signature VERA25-likeSignature VERA25-like: 0.720.72Différence de sonde / géométr…Différence de sonde / géométrie: 0.650.65Dataset multi-régimesDataset multi-régimes: 0.640.64Spectre hors domaine valideSpectre hors domaine valide: 0.620.62
DiagnosticScoreForceSignauxInterprétation probable
Splice / raccord détecteursX0.90forteSpike rate 1.00, Jump rate 1.00, RMS/SAM référence 1.00Rupture aux jonctions de détecteurs, calibration locale ou sonde différente.
Erreur calibration / référence blancheX0.81forteBaseline/mean/area 1.00, RMS/SAM référence 1.00, artefacts locaux 1.00Décalage systématique entre campagnes, instruments ou référence blanche.
Fond différentX0.75forteBaseline/mean/area 1.00, RMS/SAM référence 1.00, PCA Q 0.82Effet systématique du support, blanc/noir, transflectance ou environnement de mesure.
Erreur interpolation / rééchantillonnageX0.73forteSpike rate 1.00, Jump rate 1.00, SNR normal/élevé 1.00Artefacts numériques ou traitement spectral incorrect.
Signature VERA25-likeX0.72moyenneSpike rate 1.00, Jump rate 1.00, RMS/SAM référence 1.00Combinaison possible changement de sonde + splice, amplifiée par géométrie, fond ou calibration.
Différence de sonde / géométrieX0.65moyenneBaseline/mean/area 1.00, RMS/SAM référence 1.00, PCA Q 0.82Modification de l'illumination, collecte, angle ou distance sonde-échantillon.
Dataset multi-régimesX0.64moyenneStructure PCA 1.00, RMS/SAM référence 1.00, PCA Q 0.82Mélange de campagnes, opérateurs, lots, setups ou sous-populations spectrales.
Spectre hors domaine valideX0.62moyenneRMS/SAM référence 1.00, Structure PCA 1.00, Mahalanobis / T2 0.70Variété, espèce, lot ou condition différente mais physiquement plausible.

Spectral sources

Filtered Asphalt Brick Spectra.xlsx

X · NIR · Ocean Optics JAZ-EL350
Filtered Asphalt Brick Spectra.xlsx spectra01020300.40.60.81.0q05-q95 envelopeq25-q75 envelopemedian spectrummedianq25–q75q05–q95wavelength / none0.45none — median 7.586 (q25–q75 6.373–9.572)0.45392none — median 7.614 (q25–q75 6.404–9.652)0.45784none — median 7.638 (q25–q75 6.434–9.721)0.46176none — median 7.658 (q25–q75 6.467–9.787)0.46373none — median 7.668 (q25–q75 6.483–9.835)0.46765none — median 7.685 (q25–q75 6.518–9.933)0.47157none — median 7.703 (q25–q75 6.556–10.03)0.47549none — median 7.725 (q25–q75 6.596–10.12)0.47941none — median 7.751 (q25–q75 6.641–10.21)0.48333none — median 7.784 (q25–q75 6.69–10.3)0.48529none — median 7.802 (q25–q75 6.717–10.35)0.48922none — median 7.853 (q25–q75 6.773–10.46)0.49314none — median 7.924 (q25–q75 6.809–10.58)0.49706none — median 7.997 (q25–q75 6.833–10.69)0.50098none — median 8.071 (q25–q75 6.86–10.81)0.5049none — median 8.145 (q25–q75 6.89–10.89)0.50686none — median 8.181 (q25–q75 6.91–10.93)0.51078none — median 8.255 (q25–q75 6.963–11.03)0.51471none — median 8.33 (q25–q75 7.035–11.13)0.51863none — median 8.409 (q25–q75 7.125–11.25)0.52255none — median 8.492 (q25–q75 7.173–11.38)0.52647none — median 8.578 (q25–q75 7.222–11.52)0.52843none — median 8.622 (q25–q75 7.247–11.59)0.53235none — median 8.709 (q25–q75 7.301–11.72)0.53627none — median 8.809 (q25–q75 7.36–11.88)0.5402none — median 8.941 (q25–q75 7.425–12.04)0.54412none — median 9.088 (q25–q75 7.477–12.17)0.54804none — median 9.253 (q25–q75 7.658–12.3)0.55none — median 9.341 (q25–q75 7.744–12.37)0.55392none — median 9.528 (q25–q75 7.776–12.49)0.55784none — median 9.727 (q25–q75 7.793–12.61)0.56176none — median 10.13 (q25–q75 7.815–12.93)0.56569none — median 10.42 (q25–q75 8.131–13.16)0.56961none — median 10.51 (q25–q75 8.58–13.31)0.57157none — median 10.6 (q25–q75 8.69–13.49)0.57549none — median 10.81 (q25–q75 8.793–13.67)0.57941none — median 10.94 (q25–q75 8.871–14.12)0.58333none — median 11.13 (q25–q75 8.943–15.03)0.58725none — median 11.43 (q25–q75 9.014–15.42)0.59118none — median 11.77 (q25–q75 9.086–15.66)0.59314none — median 11.96 (q25–q75 9.12–15.69)0.59706none — median 12.25 (q25–q75 9.168–15.75)0.60098none — median 12.41 (q25–q75 9.248–15.81)0.6049none — median 12.54 (q25–q75 9.294–15.86)0.60882none — median 12.72 (q25–q75 9.336–15.98)0.61275none — median 12.89 (q25–q75 9.377–16.09)0.61471none — median 12.96 (q25–q75 9.399–16.13)0.61863none — median 13.1 (q25–q75 9.446–16.18)0.62255none — median 13.19 (q25–q75 9.5–16.22)0.62647none — median 13.28 (q25–q75 9.56–16.26)0.63039none — median 13.39 (q25–q75 9.621–16.31)0.63431none — median 13.49 (q25–q75 9.678–16.35)0.63627none — median 13.53 (q25–q75 9.702–16.38)0.6402none — median 13.62 (q25–q75 9.747–16.43)0.64412none — median 13.71 (q25–q75 9.788–16.49)0.64804none — median 13.79 (q25–q75 9.834–16.55)0.65196none — median 13.87 (q25–q75 9.885–16.63)0.65588none — median 13.96 (q25–q75 9.932–16.75)0.65784none — median 14.01 (q25–q75 9.95–16.8)0.66176none — median 14.07 (q25–q75 9.976–16.9)0.66569none — median 14.13 (q25–q75 9.994–16.97)0.66961none — median 14.19 (q25–q75 10.02–17.03)0.67353none — median 14.24 (q25–q75 10.06–17.09)0.67745none — median 14.29 (q25–q75 10.12–17.16)0.67941none — median 14.32 (q25–q75 10.15–17.2)0.68333none — median 14.38 (q25–q75 10.23–17.29)0.68725none — median 14.48 (q25–q75 10.33–17.37)0.69118none — median 14.64 (q25–q75 10.51–17.45)0.6951none — median 14.83 (q25–q75 10.7–17.53)0.69902none — median 14.99 (q25–q75 10.89–17.61)0.70098none — median 15.06 (q25–q75 10.94–17.65)0.7049none — median 15.15 (q25–q75 11.02–17.7)0.70882none — median 15.24 (q25–q75 11.19–17.76)0.71275none — median 15.31 (q25–q75 11.34–17.85)0.71667none — median 15.39 (q25–q75 11.47–17.96)0.72059none — median 15.49 (q25–q75 11.63–18.08)0.72255none — median 15.54 (q25–q75 11.73–18.13)0.72647none — median 15.62 (q25–q75 11.79–18.27)0.73039none — median 15.75 (q25–q75 11.83–18.42)0.73431none — median 15.87 (q25–q75 11.87–18.57)0.73824none — median 16 (q25–q75 11.87–18.7)0.74216none — median 16.12 (q25–q75 11.87–18.82)0.74412none — median 16.17 (q25–q75 11.87–18.88)0.74804none — median 16.26 (q25–q75 11.87–18.99)0.75196none — median 16.22 (q25–q75 11.9–19.08)0.75588none — median 16.19 (q25–q75 11.95–19.19)0.7598none — median 16.3 (q25–q75 12–19.3)0.76373none — median 16.58 (q25–q75 12.01–19.4)0.76569none — median 16.63 (q25–q75 12.01–19.45)0.76961none — median 16.72 (q25–q75 11.98–19.53)0.77353none — median 16.85 (q25–q75 11.96–19.59)0.77745none — median 16.96 (q25–q75 11.97–19.66)0.78137none — median 17.06 (q25–q75 12.01–19.71)0.78529none — median 17.12 (q25–q75 12.07–19.75)0.78725none — median 17.16 (q25–q75 12.1–19.77)0.79118none — median 17.22 (q25–q75 12.15–19.85)0.7951none — median 17.22 (q25–q75 12.15–19.93)0.79902none — median 17.2 (q25–q75 12.12–19.99)0.80294none — median 17.16 (q25–q75 12.09–20.04)0.80686none — median 17.11 (q25–q75 12.07–20.09)0.80882none — median 17.09 (q25–q75 12.06–20.12)0.81275none — median 17.07 (q25–q75 12.04–20.18)0.81667none — median 17.07 (q25–q75 12.03–20.25)0.82059none — median 17 (q25–q75 12.02–20.29)0.82451none — median 16.91 (q25–q75 12.01–20.31)0.82843none — median 16.83 (q25–q75 11.89–20.31)0.83039none — median 16.8 (q25–q75 11.81–20.31)0.83431none — median 16.77 (q25–q75 11.64–20.29)0.83824none — median 16.86 (q25–q75 11.49–20.29)0.84216none — median 16.94 (q25–q75 11.36–20.3)0.84608none — median 16.97 (q25–q75 11.22–20.27)0.85none — median 16.85 (q25–q75 11.07–20.23)0.85196none — median 16.83 (q25–q75 10.99–20.2)0.85588none — median 16.74 (q25–q75 10.85–20.18)0.8598none — median 16.66 (q25–q75 10.7–20.15)0.86373none — median 16.59 (q25–q75 10.58–20.12)0.86765none — median 16.53 (q25–q75 10.41–20.15)0.87157none — median 16.49 (q25–q75 10.3–20.17)0.87353none — median 16.48 (q25–q75 10.25–20.14)0.87745none — median 16.45 (q25–q75 10.12–20.06)0.88137none — median 16.41 (q25–q75 9.953–20.02)0.88529none — median 16.35 (q25–q75 9.813–20.07)0.88922none — median 16.38 (q25–q75 9.732–20.05)0.89314none — median 16.37 (q25–q75 9.712–20.11)0.8951none — median 16.34 (q25–q75 9.724–20.14)0.89902none — median 16.27 (q25–q75 9.684–20.18)0.90294none — median 16.34 (q25–q75 9.691–20.23)0.90686none — median 16.4 (q25–q75 9.707–20.3)0.91078none — median 16.47 (q25–q75 9.633–20.36)0.91471none — median 16.52 (q25–q75 9.608–20.39)0.91667none — median 16.53 (q25–q75 9.606–20.37)0.92059none — median 16.51 (q25–q75 9.563–20.34)0.92451none — median 16.42 (q25–q75 9.818–20.45)0.92843none — median 16.33 (q25–q75 10.13–20.53)0.93235none — median 16 (q25–q75 9.719–20.1)0.93627none — median 16.06 (q25–q75 9.443–19.89)0.93824none — median 16.02 (q25–q75 9.247–19.95)0.94216none — median 16.22 (q25–q75 9.157–20.57)0.94608none — median 16.5 (q25–q75 9.647–21.15)0.95none — median 16.69 (q25–q75 9.982–21.29)

Sampling

Wavelengths256
Axis range0.45–0.95 none
Mean spacing0.00196 none
Griduniform
Observations60

Signal & quality

Value range3.27 – 30.3
Mean range8.44 – 16.4
Mean level13.61
Area6.807
PTP7.955
Noise RMS0.0011851
SNR1.1e+04
SNR dB8e+01 dB
Dynamic range7.95
Smoothness0.01919
Saturated0.0%
X-outliers19

Integrity & artefacts

NaN ratio0.00%
Inf count0
Zero ratio0.00%
Spike count930
Spike rate6.10%
Jump count377
Jump rate2.46%
Clip fraction0.01%

Shape & reference

Baseline slope8.0753
Curvature RMS0.015952
D1 RMS0.065097
RMS to mean3.8338
RMS p9510.131
SAM to mean0.092479
SAM p950.20704
Affine offset p9515.493
Affine gain p95 Δ1.4428
Affine residual p951.4678
Xcorr lag p9525

Outliers & repeatability

PCA Q p95/median6.6
Hotelling T2 p95/median5.6
Mahalanobis H p95/median2.4
Repeat groups0

Dimensionality (PCA)

Effective rank1.4
PCs → 95% var2
PCs → 99% var3
Top-10 cum. var100.0%
Computed metric scores 29worst 1.00
FamilleMétrique calculéeValeurScoreNiveauInterprétation datasetCauses typiquesCalcul / scoring
Intégrité des donnéesNaN ratiointegrity.nan_ratio0%0.00faibleSpectre completErreur acquisition/exportcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des donnéesInf countintegrity.inf_count00.00faibleNormalCalculs invalidescount(isinf(X))alert = min(1, inf_count / 1)
Intégrité des donnéesZero ratiointegrity.zero_ratio0%0.00faibleNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceamplitude.mean_reflectance13.6071.00fortValeur atypique: Trop clair / fond visible ou Trop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveamplitude.area_under_curve6.80681.00fortValeur atypique: Différence d'éclairement ou NormalDistance sondetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globalePeak-to-peak (PTP)amplitude.peak_to_peak7.95460.00faibleVariabilité forteSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceamplitude.variance36.8220.00faibleNormal ou hétérogèneMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSnoise.noise_rms0.00118510.00faibleStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRnoise.snr114830.00faibleBon signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRnoise.bandwise_snr_min436.660.00faibleZone fiableDétecteurmin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locauxSpike countartefacts.spike_count9301.00fortArtefactsCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateartefacts.spike_rate6.1%1.00fortSpectre suspectInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countartefacts.jump_count3771.00fortRaccord détecteurSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateartefacts.jump_rate2.46%1.00fortProblème spectralCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionartefacts.clip_fraction0.013%0.01faibleNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopeshape.baseline_slope8.07531.00fortDériveÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSshape.curvature_rms0.0159520.12faibleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSshape.d1_rms0.0650970.10faiblePlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)outliers.pca_q_ratio6.55340.82fortSpectre atypiqueArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²outliers.hotelling_t2_ratio5.63670.70moyenExtrême mais cohérentVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis Houtliers.mahalanobis_h_ratio2.37420.59moyenOutlier globalDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumreference.rms_to_mean_spectrum_p9510.1311.00fortSpectre différentDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référenceSpectral Angle Mapper (SAM)reference.sam_to_mean_spectrum_p950.207040.59moyenForme différenteFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDrepeatability.rms_intra_id—0.00faibleStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDrepeatability.sam_intra_id—0.00faibleStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDrepeatability.cv_intra_id—0.00faibleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densitystructure.pca_score_density0.0488261.00fortSous-populationsLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)structure.local_outlier_factor_p953.27621.00fortSpectre isoléCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scorestructure.isolation_forest_score_p950.634691.00fortSpectre atypiqueDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
X PCA score plot-200-1000100200-50-2502550PC1 -20.24 · PC2 -5.659PC1 118.9 · PC2 5.516PC1 46.41 · PC2 -20.18PC1 52.14 · PC2 -18.57PC1 107.8 · PC2 -15.61PC1 101.9 · PC2 -17.03PC1 -176.2 · PC2 -32.49PC1 -68.22 · PC2 -25.12PC1 -69.8 · PC2 -25.41PC1 95.29 · PC2 -8.798PC1 8.801 · PC2 -15.34PC1 -38.02 · PC2 0.9225PC1 -34.61 · PC2 -3.22PC1 142.1 · PC2 -7.248PC1 140.5 · PC2 -6.281PC1 141.5 · PC2 -4.764PC1 -120.2 · PC2 -31.25PC1 23.57 · PC2 -18.39PC1 -180.2 · PC2 -49.15PC1 -180.7 · PC2 -49.1PC1 136.1 · PC2 -2.672PC1 136.6 · PC2 -8.594PC1 133.5 · PC2 2.057PC1 -8.992 · PC2 -18.29PC1 -60.64 · PC2 -9.077PC1 -59.76 · PC2 -9.198PC1 -38.51 · PC2 -24.22PC1 -58.7 · PC2 -19.21PC1 60.08 · PC2 -16.21PC1 -33.46 · PC2 -18.37PC1 72.55 · PC2 -3.223PC1 19.51 · PC2 -1.866PC1 42.49 · PC2 -3.523PC1 43.12 · PC2 -3.79PC1 -96.83 · PC2 24.3PC1 62.69 · PC2 -1.849PC1 -56.64 · PC2 18.73PC1 -155.1 · PC2 41.53PC1 78.61 · PC2 8.598PC1 88.68 · PC2 -1.17PC1 -107.3 · PC2 40.71PC1 -105.8 · PC2 39.74PC1 -120.2 · PC2 9.712PC1 -9.514 · PC2 4.631PC1 34.48 · PC2 -3.642PC1 2.801 · PC2 11.48PC1 4.965 · PC2 8.349PC1 57.75 · PC2 -6.232PC1 -13.69 · PC2 10.75PC1 -13.36 · PC2 13.11PC1 -70.82 · PC2 44.8PC1 -2.059 · PC2 27.69PC1 -59.44 · PC2 -1.865PC1 -12.33 · PC2 1.844PC1 21.81 · PC2 21.44PC1 23.94 · PC2 20.09PC1 -25.79 · PC2 41.42PC1 -24.89 · PC2 41.25PC1 25.15 · PC2 36.28PC1 -1.732 · PC2 31.67PC1 (91.9%)PC2 (6.2%)60 scores
PCA explained variance0%25%50%75%100%PC1: 91.9% (cumulative 91.9%)1PC2: 6.2% (cumulative 98.1%)2PC3: 1.6% (cumulative 99.7%)3PC4: 0.2% (cumulative 99.8%)4PC5: 0.1% (cumulative 99.9%)5PC6: 0.0% (cumulative 99.9%)6PC7: 0.0% (cumulative 100.0%)7PC8: 0.0% (cumulative 100.0%)8PC9: 0.0% (cumulative 100.0%)9PC10: 0.0% (cumulative 100.0%)10cumulative explained variancePC variancecumulativeprincipal component · cumulative (dashed)

Metric interpretation reference

Metric catalog 29
FamilleMétriqueCe qu’elle détecteForte valeur =Faible valeur =Causes typiquesCalcul / score
Intégrité des donnéesNaN ratioDonnées manquantesSpectre corrompuSpectre completErreur acquisition/exportcount(isnan(X)) / X.sizealert = min(1, nan_ratio / 0.05)
Intégrité des donnéesInf countValeurs infiniesCorruptionNormalCalculs invalidescount(isinf(X))alert = min(1, inf_count / 1)
Intégrité des donnéesZero ratioColonnes ou cellules nullesSpectre tronquéNormalExport, saturationcount(X == 0) / count(finite X)alert = min(1, zero_ratio / 0.05)
Amplitude globaleMean reflectanceNiveau moyenTrop clair / fond visibleTrop sombreFond, géométriemean(X finite)alert reuses baseline/shape drift because absolute reflectance ranges are technology-dependent
Amplitude globaleArea under curveIntensité globaleDifférence d'éclairementNormalDistance sondetrapezoid(mean_spectrum, spectral_axis)alert reuses baseline/shape drift because area scale depends on axis and units
Amplitude globalePeak-to-peak (PTP)DynamiqueVariabilité forteSpectre platSaturationmax(mean_spectrum) - min(mean_spectrum)alert increases when dynamic range is abnormally flat
Amplitude globaleVarianceVariabilité spectraleNormal ou hétérogèneSpectre platMauvais contactvar(X finite)alert increases when variance/dynamic range is abnormally flat
BruitNoise RMSBruit haute fréquenceBruitéStableLampe, détecteurmedian MAD(second derivative) * 1.4826 / sqrt(6)alert = noise_rms / signal_scale, saturated at 5%
BruitSNRQualité signalBon signalMauvais signalAcquisitionmean(abs(X)) / noise_rmsalert decreases with SNR dB; >=40 dB is treated as low alert
BruitBandwise SNRBruit localiséZone fiableZone problématiqueDétecteurmin(abs(mean_spectrum) / local second-derivative noise)alert decreases with worst-band SNR dB; >=35 dB is treated as low alert
Artefacts locauxSpike countPics étroitsArtefactsSpectre propreCosmic rays, splicecount robust outliers in second derivativealert follows spike_rate, saturated at 1%
Artefacts locauxSpike rateDensité de picsSpectre suspectNormalInterpolationspike_count / (n_samples * (n_features - 2))alert = min(1, spike_rate / 0.01)
Artefacts locauxJump countDiscontinuitésRaccord détecteurContinuSplicecount robust outliers in first derivativealert follows jump_rate, saturated at 1%
Artefacts locauxJump rateFréquence de sautsProblème spectralNormalCalibrationjump_count / (n_samples * (n_features - 1))alert = min(1, jump_rate / 0.01)
Artefacts locauxClip fractionSaturationClippingNormalDétecteur saturéfraction of finite cells equal to repeated min/max extremaalert = min(1, clip_fraction / 0.01)
Forme spectraleBaseline slopePente globaleDériveStableÉclairementlinear slope of mean_spectrum over normalized axisalert = abs(slope / signal_scale), saturated at 0.5
Forme spectraleCurvature RMSCourbureForme inhabituelleLisseFond, splicemedian RMS(second derivative per spectrum)alert = curvature_rms / signal_scale, saturated at 1%
Forme spectraleD1 RMSVariabilité localeSpectre structuréPlatBiologie ou artefactmedian RMS(first derivative per spectrum)alert = d1_rms / signal_scale, saturated at 5%
Outliers multivariésPCA Q (SPE)Non expliqué par PCASpectre atypiqueConformeArtefact, mélangep95(Q/SPE residual) / median(Q/SPE residual)alert = min(1, pca_q_ratio / 8)
Outliers multivariésHotelling T²Extrême dans PCAExtrême mais cohérentCentralVariabilité naturellep95(Hotelling T2) / median(Hotelling T2)alert = min(1, hotelling_t2_ratio / 8)
Outliers multivariésMahalanobis HDistance au nuageOutlier globalPopulation normaleDomaine différentp95(sqrt(T2)) / median(sqrt(T2))alert = min(1, mahalanobis_h_ratio / 4)
Comparaison à référenceRMS to mean spectrumDistance moyenneSpectre différentTypiqueDomain shiftp95 RMS distance to dataset mean spectrumalert = RMS_p95 / signal_scale, saturated at 25%
Comparaison à référenceSpectral Angle Mapper (SAM)Différence de formeForme différenteSimilaireFond, géométriep95 spectral angle to dataset mean spectrumalert = min(1, SAM_p95 / 0.35 rad)
RépétabilitéRMS intra-IDReproductibilitéMauvaise répétabilitéStablePositionnementmedian RMS distance to repeated-sample centroidalert = RMS_intra_ID / signal_scale, saturated at 10%
RépétabilitéSAM intra-IDVariation de formeInstableStableAcquisitionmedian SAM to repeated-sample centroidalert = min(1, SAM_intra_ID / 0.15 rad)
RépétabilitéCV intra-IDVariabilité interneMauvais contrôleStableOpérateurmedian within-ID band CValert = min(1, CV_intra_ID / 0.25)
Structure du datasetPCA score densityClustersSous-populationsHomogèneLots différents1 / median kNN distance in PCA score spacealert follows density_cv/profile structure complexity, not raw density alone
Structure du datasetLocal Outlier Factor (LOF)Anomalie localeSpectre isoléPopulation normaleCas raresp95 approximate LOF from PCA-score kNN distancesalert = min(1, max(0, LOF_p95 - 1) / 2)
Structure du datasetIsolation Forest scoreAnomalie globaleSpectre atypiqueNormalDiverses causesp95 IsolationForest anomaly score on PCA scoresalert follows structure complexity; raw score is implementation-dependent
Technology-specific extensions
TechnologieAdaptations / métriquesAnomalies cibléesCommentaire pratique
UV-Vis 300-1000 nmBaseline, pente globale, dérive aux bords 300-350 et 900-1000; métriques par zonesLumière parasite, mauvais blanc, saturation, faible signal aux extrémitésLes bords sont souvent instables; calculer aussi des scores edge/middle.
UV-Vis 300-1000 nmSaturation / clipping proche absorbance max ou réflectance maxSignal écrêtéTrès important si absorption forte.
UV-Vis 300-1000 nmRed-edge, position de maximum, ratios de bandes si végétalDécalage biologique ou artefact optiqueAide à distinguer changement réel et problème d'acquisition.
UV-Vis 300-1000 nmSmoothness / roughness indexBruit haute fréquenceSouvent plus informatif que le SNR seul.
MIR / ATR-FTIRATR contact quality index: intensité globale, aire totale, profondeur des bandes clésMauvais contact cristal-échantillonCrucial: beaucoup d'anomalies viennent du contact ATR.
MIR / ATR-FTIRCO2 / H2O atmospheric bandsMauvaise correction atmosphériquePics parasites fréquents.
MIR / ATR-FTIRBaseline curvature / rubber-band residualDiffusion, contact, dérive baselineTrès utile avant PCA.
MIR / ATR-FTIRPeak position shiftMauvais alignement spectral / calibrationImportant en FTIR car de petits shifts comptent.
MIR / ATR-FTIRBand area ratios sur bandes connuesSpectre chimiquement incohérentÀ adapter par matrice: polysaccharides, protéines, lipides, etc.
HS-MSTotal Ion Current (TIC), Base Peak Intensity (BPI)Injection faible, ionisation instableÉquivalent MS du niveau global spectral.
HS-MSNombre de pics détectésSpectre pauvre ou trop bruitéTrop peu = mauvais signal; trop = bruit/contamination.
HS-MSMass accuracy / m/z driftProblème calibration masseFondamental en HRMS.
HS-MSRetention time drift si LC/GC-MSDérive chromatographiqueÀ suivre sur standards/QC pools.
HS-MSBlank contamination scoreContaminants / carry-overComparer échantillons vs blancs.
HS-MSInternal standard CVVariabilité instrumentaleTrès robuste si standards disponibles.
HS-MSMissingness par featureInstabilité de détectionCrucial pour filtrer les variables.
Avec répétitionsRMS intra-échantillonRépétabilité globaleApplicable à toutes les technologies.
Avec répétitionsSAM / corrélation intra-échantillonRépétabilité de formeTrès utile pour spectres.
Avec répétitionsCV intra-échantillon par bande / featureRépétabilité localeDétecte les zones instables.
Avec répétitionsICC ou variance componentsPart variance échantillon vs techniqueTrès utile si plusieurs répétitions par sample.
Avec répétitionsDistance au centroïde intra-IDRépétition aberrantePermet de flagger la mauvaise répétition plutôt que le sample entier.
Bug-hunting / supervised audits
Famille de bug potentielMéthodes à ajouterCe que ça détecteÉtat dans l’explorateur
Shift spectral globalCorrélation spectre moyen inter-dataset, DTW, cross-correlation, comparaison positions de picsDécalage en longueur d'onde, mauvais alignement, interpolation différentePartiellement calculé: cross-correlation lag et dispersion des positions de pics vs spectre moyen.
Baseline / offset / gainRégression chaque spectre vs spectre moyen: x = a + b ref + residual; suivi de a, b, RMS résiduelOffset additif, effet multiplicatif, dérive de baselineCalculé dans reference.affine_*.
Mélange de lignes / mauvais appariement X-M-YVérification index, hash des lignes, duplication ID, distance spectrale intra-ID, labels incohérentsLignes mélangées, metadata mal alignées, Y attribué au mauvais spectrePartiellement couvert par répétabilité intra-ID; checks index/hash à ajouter au pipeline canonical.
Fuite d'information / répétitions mal splitéesGroupKFold par sample_id vs StratifiedKFold random; audit des partitions par sample_idPerformance artificiellement bonne due aux répétitionsNécessite splits et benchmark modèle; non calculé par la carte descriptive.
Label bugsÉchantillons proches en X mais Y différents, confident learning, erreurs systématiques FP/FNY inversés, erreurs de saisie, classes ambiguësNécessite Y et/ou modèle; recommandé pour l'explorateur supervisé.
Sous-domaines cachésPCA/UMAP/t-SNE + clustering non supervisé + association avec dataset/Y/date/operatorLots, campagnes, sondes, backgrounds non renseignésPartiellement calculé par structure PCA/LOF; UMAP/t-SNE hors carte statique.
Artefacts localisés inconnusCarte wavelength x dataset: différence moyenne, différence variance, KS par longueur d'ondeRégions spectrales anormales non anticipéesÀ calculer au niveau banque quand plusieurs datasets partagent un axe spectral.
Ruptures instrumentalesDiscontinuités dans dérivées, changepoint detectionSplice, raccord détecteur, saut local non prévuCalculé par jump/spike rates; changepoint plus avancé à ajouter.
Mélange / contamination spectraleNMF / unmixing / reconstruction par convex hullComposante externe: fond, plastique, solNon calculé automatiquement; nécessite hypothèses de composants ou grande bibliothèque.
Features instables mais prédictivesImportance modèle vs instabilité QC par variableModèle qui apprend un artefact plutôt qu'un signal biologiqueNécessite modèle supervisé; recommandé pour rapports de benchmark.

Variables

Targets 1

material

target · categorical
material classesAsphaltAsphalt: 3232Brick/PaverBrick/Paver: 2828
n / missing60 / 0
Classes2
Balance (entropy)1
Imbalance ratio1
Top classAsphalt (32)

Metadata 5

date

metadata · categorical
date classes2017-11-08 00:00:002017-11-08 00:00:00: 20202017-11-14 00:00:002017-11-14 00:00:00: 19192017-11-07 00:00:002017-11-07 00:00:00: 13132017-11-10 00:00:002017-11-10 00:00:00: 88
n / missing60 / 0
Classes4
Balance (entropy)0.96
Imbalance ratio2
Top class2017-11-08 00:00:00 (20)

location

metadata · categorical
location classes207 East 6th St., Dayton, OH207 East 6th St., Dayton, OH: 88Xenia Station, 150 N Miami Av…Xenia Station, 150 N Miami Ave, Xenia, OH: 88Riverscape Metropark, Datyon,…Riverscape Metropark, Datyon, OH: 772 Hess St., Dayton, OH2 Hess St., Dayton, OH: 77642 Milburn Ave, Dayton, OH642 Milburn Ave, Dayton, OH: 66Wittenburg University, Spring…Wittenburg University, Springfield, OH: 6682 Van Buren St., Dayton, OH82 Van Buren St., Dayton, OH: 55Beavercreek Community Park, B…Beavercreek Community Park, Beavercreek, OH: 55The Oval, Ohio State Universi…The Oval, Ohio State University: 33Ohio Stadium, Ohio State Univ…Ohio Stadium, Ohio State University: 33+1 more+1 more: 22
n / missing60 / 0
Classes11
Balance (entropy)0.97
Imbalance ratio4
Top class207 East 6th St., Dayton, OH (8)

latitude

metadata · numeric
latitude distribution0102039.68 – 39.69: 839.69 – 39.71: 039.71 – 39.72: 539.72 – 39.73: 039.73 – 39.75: 039.75 – 39.76: 2039.76 – 39.78: 1339.78 – 39.79: 039.79 – 39.8: 039.8 – 39.82: 039.82 – 39.83: 039.83 – 39.84: 039.84 – 39.86: 039.86 – 39.87: 039.87 – 39.88: 039.88 – 39.9: 039.9 – 39.91: 039.91 – 39.92: 039.92 – 39.94: 639.94 – 39.95: 039.95 – 39.97: 039.97 – 39.98: 039.98 – 39.99: 039.99 – 40.01: 839.639.739.839.940.040.1
n / missing60 / 0
Mean ± SD39.8 ± 0.104
Median39.76
Range39.68 – 40.01
CV0.00261
Skew / kurtosis1.1 / -0.19
Normal?no

longitude

metadata · numeric
longitude distribution02040-84.19 – -84.14: 33-84.14 – -84.09: 0-84.09 – -84.04: 0-84.04 – -83.99: 5-83.99 – -83.94: 0-83.94 – -83.9: 8-83.9 – -83.85: 0-83.85 – -83.8: 6-83.8 – -83.75: 0-83.75 – -83.7: 0-83.7 – -83.65: 0-83.65 – -83.6: 0-83.6 – -83.55: 0-83.55 – -83.5: 0-83.5 – -83.45: 0-83.45 – -83.4: 0-83.4 – -83.35: 0-83.35 – -83.31: 0-83.31 – -83.26: 0-83.26 – -83.21: 0-83.21 – -83.16: 0-83.16 – -83.11: 0-83.11 – -83.06: 0-83.06 – -83.01: 8-84.5-84.0-83.5-83.0
n / missing60 / 0
Mean ± SD-83.95 ± 0.389
Median-84.18
Range-84.19 – -83.01
CV0.00463
Skew / kurtosis1.8 / 1.8
Normal?no

comment

metadata · categorical
comment classeswornworn: 33resurfacedresurfaced: 22very newvery new: 22moved leaf after picture but …moved leaf after picture but before scan: 22recently resurfaced but crack…recently resurfaced but cracked: 22all lightall light: 22dirty and worndirty and worn: 22playground red brickplayground red brick: 22darker/worn/some mossdarker/worn/some moss: 22very red/smoothvery red/smooth: 22+10 more+10 more: 1010
n / missing60 / 11
Classes38
Balance (entropy)0.98
Imbalance ratio3
Top classworn (3)
Constant metadata 9
  • instrumentOcean Optics JAZ-EL350
  • signal_typereflectance
  • axis_unitum
  • axis_min0.45
  • axis_max0.95
  • n_points_original256
  • rights_statusmanual_review_needed
  • usage_scopeprivate_use_only
  • notesSource metadata reports Processing Interpolated=no and Processing Resampled=no. No project interpolation or resampling applied.

Alignment

Alignment levelobservation
Sample id availableyes
Samples60
Observations (total)60
Reps per samplemin 1 · mean 1 · max 1

Provenance & citation

ContributorUrban Materials Spectral Library
Origin · url [open]https://data.ecosis.org/dataset/urban-materials-spectral-library
Origin · url [open]http://data.ecosis.org/dataset/6dc358cd-ce2d-4e97-920a-82a3b04c8bc2/resource/5021e0fc-5041-4863-a234-a769f075c1f1/download/filtered-asphalt-brick-spectra.xlsx
Origin · url [open]http://data.ecosis.org/dataset/6dc358cd-ce2d-4e97-920a-82a3b04c8bc2/resource/3298c8e2-6dec-4f1f-93e8-9713e8cffe21/download/filtered-asphalt-brick-metadata.xlsx
Origin · script [manual]source_to_standard.py — standardization script (maintainer-only)

Governance & integrity

Tierprivate
LicenseLicenseRef-not-cleared
Permitted useResearch and benchmarking; private use only.
Access policyManual download / private-use-only per source.
RedistributionEcoSIS package license not specified in local CKAN metadata.
Content version1.0.0
Schema / protocol2.0
Content hash7a7907afa3d915c8…
Processing hashc739ca03c965bf32…
Metadata hash7381085925372cec…

Load this dataset

# pip install nirs4all-datasets
from nirs4all_datasets import get

# private dataset — export requires a Dataverse token
ds = get("ecosis_urban_materials_spectral_library_reflectance_nirs", token="…")
X, y = ds.x(), ds.y()
print(X.shape, y.shape)

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