Hanna Meyer

14 papers C 1Journal 12Unranked 1
YearRankTypeTitle / Venue / Authors
2025 J jnl
Gated recurrent units for modelling time series of soil temperature and moisture: An assessment of performance and process reflectivity.
Environ. Model. Softw.
Maiken Baumberger, Bettina Haas, Walter Tewes, Benjamin Risse, Nele Meyer, Hanna Meyer
2025 — conf
Investigating Moran's
AGILE Conference
Jakub Nowosad, Hanna Meyer
2024 J jnl
The CAST package for training and assessment of spatial prediction models in R.
CoRR
Hanna Meyer, Marvin Ludwig, Carles Milà, Jan Linnenbrink, Fabian Schumacher
2022 J jnl
Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro.
Remote. Sens.
Alice Ziegler, Hanna Meyer, Insa Otte, Marcell K. Peters, Tim Appelhans, Christina Behler, Katrin Böhning-Gaese, Alice Classen, Florian Detsch, Jürgen Deckert, Connal Eardley, Stefan W. Ferger, Markus Fischer, Friederike Gebert, Michael Haas, Maria Helbig-Bonitz, Andreas Hemp, Claudia Hemp, Victor Kakengi, Antonia V. Mayr, Christine Ngereza, Christoph Reudenbach, Juliane Röder, Gemma Rutten, David Schellenberger Costa, Matthias Schleuning, Axel Ssymank, Ingolf Steffan-Dewenter, Joseph Tardanico, Marco Tschapka, Maximilian G. R. Vollstädt, Stephan Wöllauer, Jie Zhang, Roland Brandl, Thomas Nauss
2022 J jnl
Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data.
Remote. Sens.
Luca Kleinewillinghöfer, Pontus Olofsson, Edzer J. Pebesma, Hanna Meyer, Oliver Buck, Carsten Haub, Beatrice Eiselt
2021 J jnl
A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data.
Remote. Sens.
Lilian-Maite Lezama Valdes, Marwan Katurji, Hanna Meyer
2021 C conf
Estimating the Area of Applicability of Remote Sensing-Based Machine Learning Models with Limited Training Data.
IGARSS
Hanna Meyer, Edzer J. Pebesma
2020 J jnl
Predicting into unknown space? Estimating the area of applicability of spatial prediction models.
CoRR
Hanna Meyer, Edzer J. Pebesma
2019 J jnl
Hourly gridded air temperatures of South Africa derived from MSG SEVIRI.
Int. J. Appl. Earth Obs. Geoinformation
Hanna Meyer, Johannes Schmidt, Florian Detsch, Thomas Nauss
2019 J jnl
Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction.
CoRR
Hanna Meyer, Christoph Reudenbach, Stephan Wöllauer, Thomas Nauss
2018 J jnl
Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation.
Environ. Model. Softw.
Hanna Meyer, Christoph Reudenbach, Tomislav Hengl, Marwan Katurji, Thomas Nauss
2017 J jnl
From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information?
Int. J. Appl. Earth Obs. Geoinformation
Hanna Meyer, Lukas W. Lehnert, Yun Wang, Christoph Reudenbach, Thomas Nauss, Jörg Bendix
2016 J jnl
Automatic classification of Google Earth images for a larger scale monitoring of bush encroachment in South Africa.
Int. J. Appl. Earth Obs. Geoinformation
Annika Ludwig, Hanna Meyer, Thomas Nauss
2016 J jnl
Mapping Daily Air Temperature for Antarctica Based on MODIS LST.
Remote. Sens.
Hanna Meyer, Marwan Katurji, Tim Appelhans, Markus U. Müller, Thomas Nauss, Pierre Roudier, Peyman Zawar-Reza
redb/extractors/enum.py
← Index redb/extractors/enum.py python 
from enum import Enum


class Tag(Enum):
    BASIC_PROPERTIES = "basic_properties"
    DIEC = "detect_it_easy"
    CAPA = "capa"
    MALCONTENT = "malcontent"
    DECOMPILED = "decompiled"
    HASH = "hash"
    STRINGS = "strings"
    RELATED = "relatedness" # unused
    PE_FEATURES = "pe_features"
    PE_IMPORT = "pe_import"
    PE_OVERLAY = "pe_overlay"
    PE_SECTION = "pe_section"
    PE_RESOURCE = "pe_resource"
    PE_SIGNATURE = "pe_signature"
    PE_CERTIFICATE = "pe_certificate"
    PE_SIGNER = "pe_signer"
    PE_DOTNET = "pe_dotnet"
    PE_EMBEDDED_EXTRAS = "pe_embedded_extras"
    PE_INCONSISTENCY_TESTS = "pe_inconsistency_tests"
    DOTNET_INCONSISTENCY_TESTS = "dotnet_inconsistency_tests"
    YARA = "yara"
    # Mach-O
    MACHO_FEATURES = "macho_features"
    MACHO_IMPORT = "macho_import"
    MACHO_EXPORT = "macho_export"
    MACHO_DYLIB = "macho_dylib"
    MACHO_SEGMENT = "macho_segment"
    MACHO_SIGNATURE = "macho_signature"
    MACHO_CERTIFICATE = "macho_certificate"
    MACHO_ENTITLEMENT = "macho_entitlement"
    MACHO_UNIVERSAL = "macho_universal"
    MACHO_HASHES = "macho_hashes"
    # ELF
    ELF_FEATURES = "elf_features"
    ELF_IMPORT = "elf_import"
    ELF_EXPORT = "elf_export"
    ELF_SECTION = "elf_section"
    ELF_SEGMENT = "elf_segment"
    ELF_SYMBOL = "elf_symbol"
    ELF_RELOCATION = "elf_relocation"
    ELF_NOTE = "elf_note"
    ELF_DEPENDENCY = "elf_dependency"
    # APK
    APK_FEATURES = "apk_features"
    APK_MANIFEST = "apk_manifest"
    APK_PERMISSIONS = "apk_permissions"
    APK_SIGNATURE = "apk_signature"
    APK_DEX = "apk_dex"
    APK_RESOURCES = "apk_resources"
    APK_NATIVE_LIBS = "apk_native_libs"
    APK_INCONSISTENCY_TESTS = "apk_inconsistency_tests"
    APK_DECOMPILED = "apk_decompiled"
    # JavaScript
    JS_FEATURES = "js_features"
    JS_SUSPICIOUS_APIS = "js_suspicious_apis"
    JS_STRINGS = "js_strings"
    JS_DEOBFUSCATION = "js_deobfuscation"
    JS_CONTENT = "js_content"